adding xgboost code
This commit is contained in:
mhjensen
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
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'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
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||||
2,
|
||||
None,
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||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
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||||
('Figure to Illustrate the Iterative Classification Process',
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||||
2,
|
||||
None,
|
||||
'___sec48'),
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||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
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('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
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('Xgboost on the Cancer Data', 2, None, '___sec56')]}
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end of tocinfo -->
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||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
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||||
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||||
</ul>
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</li>
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@@ -283,7 +283,7 @@ MathJax.Hub.Config({
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<li><a href="._DecisionTrees-bs008.html">9</a></li>
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<li><a href="._DecisionTrees-bs009.html">10</a></li>
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<li><a href="">...</a></li>
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<li><a href="._DecisionTrees-bs056.html">57</a></li>
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||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
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||||
<li><a href="._DecisionTrees-bs001.html">»</a></li>
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||||
</ul>
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<!-- ------------------- end of main content --------------- -->
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||||
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||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -288,7 +288,7 @@ given some assumptions, make predictions about the target feature value
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<li><a href="._DecisionTrees-bs009.html">10</a></li>
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||||
<li><a href="._DecisionTrees-bs010.html">11</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs002.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -266,7 +266,7 @@ This tree was produced using the Wisconsin cancer data (discussed here as well,
|
||||
<li><a href="._DecisionTrees-bs010.html">11</a></li>
|
||||
<li><a href="._DecisionTrees-bs011.html">12</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs003.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
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|
||||
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|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
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|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -274,7 +274,7 @@ node.
|
||||
<li><a href="._DecisionTrees-bs011.html">12</a></li>
|
||||
<li><a href="._DecisionTrees-bs012.html">13</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs004.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -275,7 +275,7 @@ Then we are essentially done!
|
||||
<li><a href="._DecisionTrees-bs012.html">13</a></li>
|
||||
<li><a href="._DecisionTrees-bs013.html">14</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs005.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
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|
||||
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|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -354,7 +354,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs013.html">14</a></li>
|
||||
<li><a href="._DecisionTrees-bs014.html">15</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs006.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
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|
||||
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|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
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|
||||
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|
||||
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|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
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|
||||
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|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -287,7 +287,7 @@ within box \( j \).
|
||||
<li><a href="._DecisionTrees-bs014.html">15</a></li>
|
||||
<li><a href="._DecisionTrees-bs015.html">16</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs007.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -279,7 +279,7 @@ better tree in some future step.
|
||||
<li><a href="._DecisionTrees-bs015.html">16</a></li>
|
||||
<li><a href="._DecisionTrees-bs016.html">17</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs008.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -312,7 +312,7 @@ region contains more than five observations.
|
||||
<li><a href="._DecisionTrees-bs016.html">17</a></li>
|
||||
<li><a href="._DecisionTrees-bs017.html">18</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs009.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
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|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
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|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -281,7 +281,7 @@ parameter \( \alpha \).
|
||||
<li><a href="._DecisionTrees-bs017.html">18</a></li>
|
||||
<li><a href="._DecisionTrees-bs018.html">19</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs010.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
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'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
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('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
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('Random forests', 2, None, '___sec40'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec43'),
|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec45'),
|
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|
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|
||||
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|
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|
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|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -294,7 +294,7 @@ subtree corresponding to \( \alpha \).
|
||||
<li><a href="._DecisionTrees-bs018.html">19</a></li>
|
||||
<li><a href="._DecisionTrees-bs019.html">20</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs011.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
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|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
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|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
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|
||||
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|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
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|
||||
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|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
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|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -290,7 +290,7 @@ MathJax.Hub.Config({
|
||||
<li><a href="._DecisionTrees-bs019.html">20</a></li>
|
||||
<li><a href="._DecisionTrees-bs020.html">21</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs012.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -282,7 +282,7 @@ fall into that region.
|
||||
<li><a href="._DecisionTrees-bs020.html">21</a></li>
|
||||
<li><a href="._DecisionTrees-bs021.html">22</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs013.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -287,7 +287,7 @@ than is the classification error rate.
|
||||
<li><a href="._DecisionTrees-bs021.html">22</a></li>
|
||||
<li><a href="._DecisionTrees-bs022.html">23</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs014.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
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|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
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|
||||
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|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
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|
||||
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|
||||
'Bagging',
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
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||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -313,7 +313,7 @@ $$
|
||||
<li><a href="._DecisionTrees-bs022.html">23</a></li>
|
||||
<li><a href="._DecisionTrees-bs023.html">24</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs015.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
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|
||||
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|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
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|
||||
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|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
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|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
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|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
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|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -304,7 +304,7 @@ os<span style="color: #666666">.</span>system(cmd)
|
||||
<li><a href="._DecisionTrees-bs023.html">24</a></li>
|
||||
<li><a href="._DecisionTrees-bs024.html">25</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs016.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
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|
||||
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|
||||
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|
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|
||||
'___sec47'),
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('Basic Steps of AdaBoost', 2, None, '___sec48'),
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('AdaBoost Examples', 2, None, '___sec49'),
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||||
('Gradient boosting: Basics', 2, None, '___sec50'),
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||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
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('Gradient Boosting, Examples', 2, None, '___sec52'),
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('Gradient Boots with Early Stopping', 2, None, '___sec53'),
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||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
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|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -295,7 +295,7 @@ os<span style="color: #666666">.</span>system(cmd)
|
||||
<li><a href="._DecisionTrees-bs024.html">25</a></li>
|
||||
<li><a href="._DecisionTrees-bs025.html">26</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs017.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
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|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
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|
||||
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|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
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|
||||
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|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -278,7 +278,7 @@ We discuss both algorithms with applications here. The popular library <b>Scikit
|
||||
<li><a href="._DecisionTrees-bs025.html">26</a></li>
|
||||
<li><a href="._DecisionTrees-bs026.html">27</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs018.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -268,7 +268,7 @@ MathJax.Hub.Config({
|
||||
<li><a href="._DecisionTrees-bs026.html">27</a></li>
|
||||
<li><a href="._DecisionTrees-bs027.html">28</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs019.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
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|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -268,7 +268,7 @@ MathJax.Hub.Config({
|
||||
<li><a href="._DecisionTrees-bs027.html">28</a></li>
|
||||
<li><a href="._DecisionTrees-bs028.html">29</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs020.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec43'),
|
||||
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|
||||
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|
||||
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|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
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|
||||
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|
||||
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|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
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|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
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|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -308,7 +308,7 @@ The table here summarizes the various attributes and
|
||||
<li><a href="._DecisionTrees-bs028.html">29</a></li>
|
||||
<li><a href="._DecisionTrees-bs029.html">30</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs021.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
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|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
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|
||||
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|
||||
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|
||||
'___sec47'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -339,7 +339,7 @@ os<span style="color: #666666">.</span>system(cmd)
|
||||
<li><a href="._DecisionTrees-bs029.html">30</a></li>
|
||||
<li><a href="._DecisionTrees-bs030.html">31</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs022.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
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|
||||
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|
||||
'___sec42'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
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|
||||
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|
||||
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|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
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|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -341,7 +341,7 @@ split <span style="color: #666666">=</span> get_split(dataset)
|
||||
<li><a href="._DecisionTrees-bs030.html">31</a></li>
|
||||
<li><a href="._DecisionTrees-bs031.html">32</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs023.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
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|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
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|
||||
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|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -299,7 +299,7 @@ attributes at each step while growing the tree.
|
||||
<li><a href="._DecisionTrees-bs031.html">32</a></li>
|
||||
<li><a href="._DecisionTrees-bs032.html">33</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs024.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
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|
||||
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|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -459,7 +459,7 @@ MathJax.Hub.Config({
|
||||
<li><a href="._DecisionTrees-bs032.html">33</a></li>
|
||||
<li><a href="._DecisionTrees-bs033.html">34</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs025.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -312,7 +312,7 @@ deep_tree_clf<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
|
||||
<li><a href="._DecisionTrees-bs033.html">34</a></li>
|
||||
<li><a href="._DecisionTrees-bs034.html">35</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs026.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -335,7 +335,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs034.html">35</a></li>
|
||||
<li><a href="._DecisionTrees-bs035.html">36</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs027.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
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|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -291,7 +291,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs035.html">36</a></li>
|
||||
<li><a href="._DecisionTrees-bs036.html">37</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs028.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
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|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
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|
||||
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|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -285,7 +285,7 @@ tree_reg<span style="color: #666666">.</span>fit(X, y)
|
||||
<li><a href="._DecisionTrees-bs036.html">37</a></li>
|
||||
<li><a href="._DecisionTrees-bs037.html">38</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs029.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -341,7 +341,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs037.html">38</a></li>
|
||||
<li><a href="._DecisionTrees-bs038.html">39</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs030.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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('Xgboost on the Cancer Data', 2, None, '___sec56')]}
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|
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<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -277,7 +277,7 @@ MathJax.Hub.Config({
|
||||
<li><a href="._DecisionTrees-bs038.html">39</a></li>
|
||||
<li><a href="._DecisionTrees-bs039.html">40</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs031.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -280,7 +280,7 @@ However, by aggregating many decision trees, using methods like bagging, random
|
||||
<li><a href="._DecisionTrees-bs039.html">40</a></li>
|
||||
<li><a href="._DecisionTrees-bs040.html">41</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs032.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -287,7 +287,7 @@ We discuss these methods here.
|
||||
<li><a href="._DecisionTrees-bs040.html">41</a></li>
|
||||
<li><a href="._DecisionTrees-bs041.html">42</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs033.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
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|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
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|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
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|
||||
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|
||||
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|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
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|
||||
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|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
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|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -271,7 +271,7 @@ MathJax.Hub.Config({
|
||||
<li><a href="._DecisionTrees-bs041.html">42</a></li>
|
||||
<li><a href="._DecisionTrees-bs042.html">43</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs034.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -282,7 +282,7 @@ learning method.
|
||||
<li><a href="._DecisionTrees-bs042.html">43</a></li>
|
||||
<li><a href="._DecisionTrees-bs043.html">44</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs035.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
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|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -292,7 +292,7 @@ predictor, averaged over all \( B \) trees.
|
||||
<li><a href="._DecisionTrees-bs043.html">44</a></li>
|
||||
<li><a href="._DecisionTrees-bs044.html">45</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs036.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
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|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
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|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
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|
||||
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|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
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|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
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|
||||
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|
||||
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|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
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|
||||
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|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
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|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -283,7 +283,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs044.html">45</a></li>
|
||||
<li><a href="._DecisionTrees-bs045.html">46</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs037.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -314,7 +314,7 @@ voting_clf<span style="color: #666666">.</span>fit(X_train, y_train)
|
||||
<li><a href="._DecisionTrees-bs045.html">46</a></li>
|
||||
<li><a href="._DecisionTrees-bs046.html">47</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs038.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
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|
||||
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|
||||
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|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
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|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
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|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
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|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -321,7 +321,7 @@ voting_clf<span style="color: #666666">.</span>fit(X_train, y_train)
|
||||
<li><a href="._DecisionTrees-bs046.html">47</a></li>
|
||||
<li><a href="._DecisionTrees-bs047.html">48</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs039.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
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'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
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|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
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|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -324,7 +324,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs047.html">48</a></li>
|
||||
<li><a href="._DecisionTrees-bs048.html">49</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs040.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
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|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
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|
||||
'___sec46'),
|
||||
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|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
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|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -323,7 +323,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs048.html">49</a></li>
|
||||
<li><a href="._DecisionTrees-bs049.html">50</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs041.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -240,47 +240,62 @@ MathJax.Hub.Config({
|
||||
<a name="part0041"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec40" class="anchor">Random forests </h2>
|
||||
<h2 id="___sec40" class="anchor">Changing the Level of the Decision Tree </h2>
|
||||
|
||||
<p>
|
||||
Random forests provide an improvement over bagged trees by way of a
|
||||
small tweak that decorrelates the trees.
|
||||
|
||||
<p>
|
||||
As in bagging, we build a
|
||||
number of decision trees on bootstrapped training samples. But when
|
||||
building these decision trees, each time a split in a tree is
|
||||
considered, a random sample of \( m \) predictors is chosen as split
|
||||
candidates from the full set of \( p \) predictors. The split is allowed to
|
||||
use only one of those \( m \) predictors.
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
|
||||
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.pipeline</span> <span style="color: #008000; font-weight: bold">import</span> make_pipeline
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.utils</span> <span style="color: #008000; font-weight: bold">import</span> resample
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeRegressor
|
||||
|
||||
<p>
|
||||
A fresh sample of \( m \) predictors is
|
||||
taken at each split, and typically we choose
|
||||
n <span style="color: #666666">=</span> <span style="color: #666666">100</span>
|
||||
n_boostraps <span style="color: #666666">=</span> <span style="color: #666666">100</span>
|
||||
maxdepth <span style="color: #666666">=</span> <span style="color: #666666">8</span>
|
||||
|
||||
$$
|
||||
m\approx \sqrt{p}.
|
||||
$$
|
||||
<span style="color: #408080; font-style: italic"># Make data set.</span>
|
||||
x <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linspace(<span style="color: #666666">-3</span>, <span style="color: #666666">3</span>, n)<span style="color: #666666">.</span>reshape(<span style="color: #666666">-1</span>, <span style="color: #666666">1</span>)
|
||||
y <span style="color: #666666">=</span> np<span style="color: #666666">.</span>exp(<span style="color: #666666">-</span>x<span style="color: #666666">**2</span>) <span style="color: #666666">+</span> <span style="color: #666666">1.5</span> <span style="color: #666666">*</span> np<span style="color: #666666">.</span>exp(<span style="color: #666666">-</span>(x<span style="color: #666666">-2</span>)<span style="color: #666666">**2</span>)<span style="color: #666666">+</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>normal(<span style="color: #666666">0</span>, <span style="color: #666666">0.1</span>, x<span style="color: #666666">.</span>shape)
|
||||
error <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdepth)
|
||||
bias <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdepth)
|
||||
variance <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdepth)
|
||||
polydegree <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdepth)
|
||||
X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(x, y, test_size<span style="color: #666666">=0.2</span>)
|
||||
|
||||
<p>
|
||||
In building a random forest, at
|
||||
each split in the tree, the algorithm is not even allowed to consider
|
||||
a majority of the available predictors.
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> StandardScaler
|
||||
scaler <span style="color: #666666">=</span> StandardScaler()
|
||||
scaler<span style="color: #666666">.</span>fit(X_train)
|
||||
X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_train)
|
||||
X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
|
||||
|
||||
<p>
|
||||
The reason for this is rather clever. Suppose that there is one very
|
||||
strong predictor in the data set, along with a number of other
|
||||
moderately strong predictors. Then in the collection of bagged
|
||||
variable importance random forest trees, most or all of the trees will
|
||||
use this strong predictor in the top split. Consequently, all of the
|
||||
bagged trees will look quite similar to each other. Hence the
|
||||
predictions from the bagged trees will be highly correlated.
|
||||
Unfortunately, averaging many highly correlated quantities does not
|
||||
lead to as large of a reduction in variance as averaging many
|
||||
uncorrelated quanti- ties. In particular, this means that bagging will
|
||||
not lead to a substantial reduction in variance over a single tree in
|
||||
this setting.
|
||||
<span style="color: #008000; font-weight: bold">for</span> degree <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #666666">1</span>,maxdepth):
|
||||
model <span style="color: #666666">=</span> DecisionTreeRegressor(max_depth<span style="color: #666666">=</span>degree)
|
||||
y_pred <span style="color: #666666">=</span> np<span style="color: #666666">.</span>empty((y_test<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>], n_boostraps))
|
||||
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(n_boostraps):
|
||||
x_, y_ <span style="color: #666666">=</span> resample(X_train_scaled, y_train)
|
||||
model<span style="color: #666666">.</span>fit(x_, y_)
|
||||
y_pred[:, i] <span style="color: #666666">=</span> model<span style="color: #666666">.</span>predict(X_test_scaled)<span style="color: #408080; font-style: italic">#.ravel()</span>
|
||||
|
||||
polydegree[degree] <span style="color: #666666">=</span> degree
|
||||
error[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> y_pred)<span style="color: #666666">**2</span>, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>) )
|
||||
bias[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( (y_test <span style="color: #666666">-</span> np<span style="color: #666666">.</span>mean(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>))<span style="color: #666666">**2</span> )
|
||||
variance[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>var(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>) )
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Polynomial degree:'</span>, degree)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Error:'</span>, error[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Bias^2:'</span>, bias[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Var:'</span>, variance[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'{} >= {} + {} = {}'</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))
|
||||
|
||||
plt<span style="color: #666666">.</span>xlim(<span style="color: #666666">1</span>,maxdepth)
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, error, label<span style="color: #666666">=</span><span style="color: #BA2121">'Error'</span>)
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, bias, label<span style="color: #666666">=</span><span style="color: #BA2121">'bias'</span>)
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, variance, label<span style="color: #666666">=</span><span style="color: #BA2121">'Variance'</span>)
|
||||
plt<span style="color: #666666">.</span>legend()
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
</pre></div>
|
||||
<p>
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
@@ -307,7 +322,7 @@ this setting.
|
||||
<li><a href="._DecisionTrees-bs049.html">50</a></li>
|
||||
<li><a href="._DecisionTrees-bs050.html">51</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs042.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -240,30 +240,48 @@ MathJax.Hub.Config({
|
||||
<a name="part0042"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec41" class="anchor">Random Forest Algorithm </h2>
|
||||
The algorithm described here can be applied to both classification and regression problems.
|
||||
<h2 id="___sec41" class="anchor">Random forests </h2>
|
||||
|
||||
<p>
|
||||
We will grow of forest of say \( M \) trees.
|
||||
Random forests provide an improvement over bagged trees by way of a
|
||||
small tweak that decorrelates the trees.
|
||||
|
||||
<ol>
|
||||
<li> For \( m=1:M \) we</li>
|
||||
<p>
|
||||
As in bagging, we build a
|
||||
number of decision trees on bootstrapped training samples. But when
|
||||
building these decision trees, each time a split in a tree is
|
||||
considered, a random sample of \( m \) predictors is chosen as split
|
||||
candidates from the full set of \( p \) predictors. The split is allowed to
|
||||
use only one of those \( m \) predictors.
|
||||
|
||||
<ul>
|
||||
<li> Draw a bootstrap sample of from the training data organized in our \( \boldsymbol{X} \) matrix.</li>
|
||||
<li> We grow then a random forest tree \( T_m \) based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached</li>
|
||||
<p>
|
||||
A fresh sample of \( m \) predictors is
|
||||
taken at each split, and typically we choose
|
||||
|
||||
<ol>
|
||||
<li> we select \( m \le p \) varibales at random from the \( p \) predictors/features</li>
|
||||
<li> pick the best split point among the \( m \) features using either the CART algorithm or the ID3 for classification and create a new node</li>
|
||||
<li> split the node into daughter nodes</li>
|
||||
</ol>
|
||||
$$
|
||||
m\approx \sqrt{p}.
|
||||
$$
|
||||
|
||||
</ul>
|
||||
<p>
|
||||
In building a random forest, at
|
||||
each split in the tree, the algorithm is not even allowed to consider
|
||||
a majority of the available predictors.
|
||||
|
||||
<li> Output then the ensemble of trees \( \{T_m\}_1^{M} \) and make predictions for either a regression type of problem or a classification type of problem.</li>
|
||||
</ol>
|
||||
<p>
|
||||
The reason for this is rather clever. Suppose that there is one very
|
||||
strong predictor in the data set, along with a number of other
|
||||
moderately strong predictors. Then in the collection of bagged
|
||||
variable importance random forest trees, most or all of the trees will
|
||||
use this strong predictor in the top split. Consequently, all of the
|
||||
bagged trees will look quite similar to each other. Hence the
|
||||
predictions from the bagged trees will be highly correlated.
|
||||
Unfortunately, averaging many highly correlated quantities does not
|
||||
lead to as large of a reduction in variance as averaging many
|
||||
uncorrelated quanti- ties. In particular, this means that bagging will
|
||||
not lead to a substantial reduction in variance over a single tree in
|
||||
this setting.
|
||||
|
||||
<p>
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
<ul class="pagination">
|
||||
@@ -289,7 +307,7 @@ We will grow of forest of say \( M \) trees.
|
||||
<li><a href="._DecisionTrees-bs050.html">51</a></li>
|
||||
<li><a href="._DecisionTrees-bs051.html">52</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs043.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -240,77 +240,30 @@ MathJax.Hub.Config({
|
||||
<a name="part0043"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec42" class="anchor">Random Forests Compared with other Methods on the Cancer Data </h2>
|
||||
<h2 id="___sec42" class="anchor">Random Forest Algorithm </h2>
|
||||
The algorithm described here can be applied to both classification and regression problems.
|
||||
|
||||
<p>
|
||||
We will grow of forest of say \( M \) trees.
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
|
||||
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_breast_cancer
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.svm</span> <span style="color: #008000; font-weight: bold">import</span> SVC
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">import</span> LogisticRegression
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeClassifier
|
||||
<ol>
|
||||
<li> For \( m=1:M \) we</li>
|
||||
|
||||
<span style="color: #408080; font-style: italic"># Load the data</span>
|
||||
cancer <span style="color: #666666">=</span> load_breast_cancer()
|
||||
<ul>
|
||||
<li> Draw a bootstrap sample of from the training data organized in our \( \boldsymbol{X} \) matrix.</li>
|
||||
<li> We grow then a random forest tree \( T_m \) based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached</li>
|
||||
|
||||
X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(cancer<span style="color: #666666">.</span>data,cancer<span style="color: #666666">.</span>target,random_state<span style="color: #666666">=0</span>)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(X_train<span style="color: #666666">.</span>shape)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(X_test<span style="color: #666666">.</span>shape)
|
||||
<span style="color: #408080; font-style: italic"># Logistic Regression</span>
|
||||
logreg <span style="color: #666666">=</span> LogisticRegression(solver<span style="color: #666666">=</span><span style="color: #BA2121">'lbfgs'</span>)
|
||||
logreg<span style="color: #666666">.</span>fit(X_train, y_train)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy with Logistic Regression: {:.2f}"</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test,y_test)))
|
||||
<span style="color: #408080; font-style: italic"># Support vector machine</span>
|
||||
svm <span style="color: #666666">=</span> SVC(gamma<span style="color: #666666">=</span><span style="color: #BA2121">'auto'</span>, C<span style="color: #666666">=100</span>)
|
||||
svm<span style="color: #666666">.</span>fit(X_train, y_train)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy with SVM: {:.2f}"</span><span style="color: #666666">.</span>format(svm<span style="color: #666666">.</span>score(X_test,y_test)))
|
||||
<span style="color: #408080; font-style: italic"># Decision Trees</span>
|
||||
deep_tree_clf <span style="color: #666666">=</span> DecisionTreeClassifier(max_depth<span style="color: #666666">=</span><span style="color: #008000">None</span>)
|
||||
deep_tree_clf<span style="color: #666666">.</span>fit(X_train, y_train)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy with Decision Trees: {:.2f}"</span><span style="color: #666666">.</span>format(deep_tree_clf<span style="color: #666666">.</span>score(X_test,y_test)))
|
||||
<span style="color: #408080; font-style: italic">#now scale the data</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> StandardScaler
|
||||
scaler <span style="color: #666666">=</span> StandardScaler()
|
||||
scaler<span style="color: #666666">.</span>fit(X_train)
|
||||
X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_train)
|
||||
X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
|
||||
<span style="color: #408080; font-style: italic"># Logistic Regression</span>
|
||||
logreg<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy Logistic Regression with scaled data: {:.2f}"</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
|
||||
<span style="color: #408080; font-style: italic"># Support Vector Machine</span>
|
||||
svm<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy SVM with scaled data: {:.2f}"</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
|
||||
<span style="color: #408080; font-style: italic"># Decision Trees</span>
|
||||
deep_tree_clf<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy with Decision Trees and scaled data: {:.2f}"</span><span style="color: #666666">.</span>format(deep_tree_clf<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
|
||||
<ol>
|
||||
<li> we select \( m \le p \) varibales at random from the \( p \) predictors/features</li>
|
||||
<li> pick the best split point among the \( m \) features using either the CART algorithm or the ID3 for classification and create a new node</li>
|
||||
<li> split the node into daughter nodes</li>
|
||||
</ol>
|
||||
|
||||
</ul>
|
||||
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> RandomForestClassifier
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> LabelEncoder
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> cross_validate
|
||||
<span style="color: #408080; font-style: italic"># Data set not specificied</span>
|
||||
<span style="color: #408080; font-style: italic">#Instantiate the model with 500 trees and entropy as splitting criteria</span>
|
||||
Random_Forest_model <span style="color: #666666">=</span> RandomForestClassifier(n_estimators<span style="color: #666666">=500</span>,criterion<span style="color: #666666">=</span><span style="color: #BA2121">"entropy"</span>)
|
||||
Random_Forest_model<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
|
||||
<span style="color: #408080; font-style: italic">#Cross validation</span>
|
||||
accuracy <span style="color: #666666">=</span> cross_validate(Random_Forest_model,X_test_scaled,y_test,cv<span style="color: #666666">=10</span>)[<span style="color: #BA2121">'test_score'</span>]
|
||||
<span style="color: #008000; font-weight: bold">print</span>(accuracy)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy with Random Forests and scaled data: {:.2f}"</span><span style="color: #666666">.</span>format(Random_Forest_model<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
|
||||
<li> Output then the ensemble of trees \( \{T_m\}_1^{M} \) and make predictions for either a regression type of problem or a classification type of problem.</li>
|
||||
</ol>
|
||||
|
||||
|
||||
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">scikitplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">skplt</span>
|
||||
y_pred <span style="color: #666666">=</span> Random_Forest_model<span style="color: #666666">.</span>predict(X_test_scaled)
|
||||
skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_confusion_matrix(y_test, y_pred, normalize<span style="color: #666666">=</span><span style="color: #008000">True</span>)
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
y_probas <span style="color: #666666">=</span> Random_Forest_model<span style="color: #666666">.</span>predict_proba(X_test_scaled)
|
||||
skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_roc(y_test, y_probas)
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_cumulative_gain(y_test, y_probas)
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
</pre></div>
|
||||
<p>
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
<ul class="pagination">
|
||||
@@ -336,7 +289,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs051.html">52</a></li>
|
||||
<li><a href="._DecisionTrees-bs052.html">53</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs044.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -240,24 +240,75 @@ MathJax.Hub.Config({
|
||||
<a name="part0044"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec43" class="anchor">Compare Bagging on Trees with Random Forests </h2>
|
||||
<h2 id="___sec43" class="anchor">Random Forests Compared with other Methods on the Cancer Data </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>bag_clf <span style="color: #666666">=</span> BaggingClassifier(
|
||||
DecisionTreeClassifier(splitter<span style="color: #666666">=</span><span style="color: #BA2121">"random"</span>, max_leaf_nodes<span style="color: #666666">=16</span>, random_state<span style="color: #666666">=42</span>),
|
||||
n_estimators<span style="color: #666666">=500</span>, max_samples<span style="color: #666666">=1.0</span>, bootstrap<span style="color: #666666">=</span><span style="color: #008000">True</span>, n_jobs<span style="color: #666666">=-1</span>, random_state<span style="color: #666666">=42</span>)
|
||||
</pre></div>
|
||||
<p>
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
|
||||
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_breast_cancer
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.svm</span> <span style="color: #008000; font-weight: bold">import</span> SVC
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">import</span> LogisticRegression
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeClassifier
|
||||
|
||||
<span style="color: #408080; font-style: italic"># Load the data</span>
|
||||
cancer <span style="color: #666666">=</span> load_breast_cancer()
|
||||
|
||||
X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(cancer<span style="color: #666666">.</span>data,cancer<span style="color: #666666">.</span>target,random_state<span style="color: #666666">=0</span>)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(X_train<span style="color: #666666">.</span>shape)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(X_test<span style="color: #666666">.</span>shape)
|
||||
<span style="color: #408080; font-style: italic"># Logistic Regression</span>
|
||||
logreg <span style="color: #666666">=</span> LogisticRegression(solver<span style="color: #666666">=</span><span style="color: #BA2121">'lbfgs'</span>)
|
||||
logreg<span style="color: #666666">.</span>fit(X_train, y_train)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy with Logistic Regression: {:.2f}"</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test,y_test)))
|
||||
<span style="color: #408080; font-style: italic"># Support vector machine</span>
|
||||
svm <span style="color: #666666">=</span> SVC(gamma<span style="color: #666666">=</span><span style="color: #BA2121">'auto'</span>, C<span style="color: #666666">=100</span>)
|
||||
svm<span style="color: #666666">.</span>fit(X_train, y_train)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy with SVM: {:.2f}"</span><span style="color: #666666">.</span>format(svm<span style="color: #666666">.</span>score(X_test,y_test)))
|
||||
<span style="color: #408080; font-style: italic"># Decision Trees</span>
|
||||
deep_tree_clf <span style="color: #666666">=</span> DecisionTreeClassifier(max_depth<span style="color: #666666">=</span><span style="color: #008000">None</span>)
|
||||
deep_tree_clf<span style="color: #666666">.</span>fit(X_train, y_train)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy with Decision Trees: {:.2f}"</span><span style="color: #666666">.</span>format(deep_tree_clf<span style="color: #666666">.</span>score(X_test,y_test)))
|
||||
<span style="color: #408080; font-style: italic">#now scale the data</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> StandardScaler
|
||||
scaler <span style="color: #666666">=</span> StandardScaler()
|
||||
scaler<span style="color: #666666">.</span>fit(X_train)
|
||||
X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_train)
|
||||
X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
|
||||
<span style="color: #408080; font-style: italic"># Logistic Regression</span>
|
||||
logreg<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy Logistic Regression with scaled data: {:.2f}"</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
|
||||
<span style="color: #408080; font-style: italic"># Support Vector Machine</span>
|
||||
svm<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy SVM with scaled data: {:.2f}"</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
|
||||
<span style="color: #408080; font-style: italic"># Decision Trees</span>
|
||||
deep_tree_clf<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy with Decision Trees and scaled data: {:.2f}"</span><span style="color: #666666">.</span>format(deep_tree_clf<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
|
||||
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>bag_clf<span style="color: #666666">.</span>fit(X_train, y_train)
|
||||
y_pred <span style="color: #666666">=</span> bag_clf<span style="color: #666666">.</span>predict(X_test)
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> RandomForestClassifier
|
||||
rnd_clf <span style="color: #666666">=</span> RandomForestClassifier(n_estimators<span style="color: #666666">=500</span>, max_leaf_nodes<span style="color: #666666">=16</span>, n_jobs<span style="color: #666666">=-1</span>, random_state<span style="color: #666666">=42</span>)
|
||||
rnd_clf<span style="color: #666666">.</span>fit(X_train, y_train)
|
||||
y_pred_rf <span style="color: #666666">=</span> rnd_clf<span style="color: #666666">.</span>predict(X_test)
|
||||
np<span style="color: #666666">.</span>sum(y_pred <span style="color: #666666">==</span> y_pred_rf) <span style="color: #666666">/</span> <span style="color: #008000">len</span>(y_pred)
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> LabelEncoder
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> cross_validate
|
||||
<span style="color: #408080; font-style: italic"># Data set not specificied</span>
|
||||
<span style="color: #408080; font-style: italic">#Instantiate the model with 500 trees and entropy as splitting criteria</span>
|
||||
Random_Forest_model <span style="color: #666666">=</span> RandomForestClassifier(n_estimators<span style="color: #666666">=500</span>,criterion<span style="color: #666666">=</span><span style="color: #BA2121">"entropy"</span>)
|
||||
Random_Forest_model<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
|
||||
<span style="color: #408080; font-style: italic">#Cross validation</span>
|
||||
accuracy <span style="color: #666666">=</span> cross_validate(Random_Forest_model,X_test_scaled,y_test,cv<span style="color: #666666">=10</span>)[<span style="color: #BA2121">'test_score'</span>]
|
||||
<span style="color: #008000; font-weight: bold">print</span>(accuracy)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">"Test set accuracy with Random Forests and scaled data: {:.2f}"</span><span style="color: #666666">.</span>format(Random_Forest_model<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
|
||||
|
||||
|
||||
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">scikitplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">skplt</span>
|
||||
y_pred <span style="color: #666666">=</span> Random_Forest_model<span style="color: #666666">.</span>predict(X_test_scaled)
|
||||
skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_confusion_matrix(y_test, y_pred, normalize<span style="color: #666666">=</span><span style="color: #008000">True</span>)
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
y_probas <span style="color: #666666">=</span> Random_Forest_model<span style="color: #666666">.</span>predict_proba(X_test_scaled)
|
||||
skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_roc(y_test, y_probas)
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_cumulative_gain(y_test, y_probas)
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
</pre></div>
|
||||
<p>
|
||||
<p>
|
||||
@@ -285,7 +336,7 @@ np<span style="color: #666666">.</span>sum(y_pred <span style="color: #666666">=
|
||||
<li><a href="._DecisionTrees-bs052.html">53</a></li>
|
||||
<li><a href="._DecisionTrees-bs053.html">54</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs045.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -240,62 +240,24 @@ MathJax.Hub.Config({
|
||||
<a name="part0045"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec44" class="anchor">Bootstrap with Random Forests Instead of a Single Tree </h2>
|
||||
|
||||
<h2 id="___sec44" class="anchor">Compare Bagging on Trees with Random Forests </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
|
||||
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.pipeline</span> <span style="color: #008000; font-weight: bold">import</span> make_pipeline
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.utils</span> <span style="color: #008000; font-weight: bold">import</span> resample
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> RandomForestRegressor
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>bag_clf <span style="color: #666666">=</span> BaggingClassifier(
|
||||
DecisionTreeClassifier(splitter<span style="color: #666666">=</span><span style="color: #BA2121">"random"</span>, max_leaf_nodes<span style="color: #666666">=16</span>, random_state<span style="color: #666666">=42</span>),
|
||||
n_estimators<span style="color: #666666">=500</span>, max_samples<span style="color: #666666">=1.0</span>, bootstrap<span style="color: #666666">=</span><span style="color: #008000">True</span>, n_jobs<span style="color: #666666">=-1</span>, random_state<span style="color: #666666">=42</span>)
|
||||
</pre></div>
|
||||
<p>
|
||||
|
||||
np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>seed(<span style="color: #666666">2018</span>)
|
||||
|
||||
n <span style="color: #666666">=</span> <span style="color: #666666">40</span>
|
||||
n_boostraps <span style="color: #666666">=</span> <span style="color: #666666">100</span>
|
||||
maxdegree <span style="color: #666666">=</span> <span style="color: #666666">14</span>
|
||||
|
||||
<span style="color: #408080; font-style: italic"># Make data set.</span>
|
||||
x <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linspace(<span style="color: #666666">-3</span>, <span style="color: #666666">3</span>, n)<span style="color: #666666">.</span>reshape(<span style="color: #666666">-1</span>, <span style="color: #666666">1</span>)
|
||||
y <span style="color: #666666">=</span> np<span style="color: #666666">.</span>exp(<span style="color: #666666">-</span>x<span style="color: #666666">**2</span>) <span style="color: #666666">+</span> <span style="color: #666666">1.5</span> <span style="color: #666666">*</span> np<span style="color: #666666">.</span>exp(<span style="color: #666666">-</span>(x<span style="color: #666666">-2</span>)<span style="color: #666666">**2</span>)<span style="color: #666666">+</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>normal(<span style="color: #666666">0</span>, <span style="color: #666666">0.1</span>, x<span style="color: #666666">.</span>shape)
|
||||
error <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdegree)
|
||||
bias <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdegree)
|
||||
variance <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdegree)
|
||||
polydegree <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdegree)
|
||||
X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(x, y, test_size<span style="color: #666666">=0.2</span>)
|
||||
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> StandardScaler
|
||||
scaler <span style="color: #666666">=</span> StandardScaler()
|
||||
scaler<span style="color: #666666">.</span>fit(X_train)
|
||||
X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_train)
|
||||
X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
|
||||
|
||||
<span style="color: #008000; font-weight: bold">for</span> degree <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(maxdegree):
|
||||
model <span style="color: #666666">=</span> RandomForestRegressor()
|
||||
y_pred <span style="color: #666666">=</span> np<span style="color: #666666">.</span>empty((y_test<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>], n_boostraps))
|
||||
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(n_boostraps):
|
||||
x_, y_ <span style="color: #666666">=</span> resample(X_train_scaled, y_train)
|
||||
model<span style="color: #666666">.</span>fit(x_, y_)
|
||||
y_pred[:, i] <span style="color: #666666">=</span> model<span style="color: #666666">.</span>predict(X_test_scaled)<span style="color: #666666">.</span>ravel()
|
||||
|
||||
polydegree[degree] <span style="color: #666666">=</span> degree
|
||||
error[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> y_pred)<span style="color: #666666">**2</span>, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>) )
|
||||
bias[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( (y_test <span style="color: #666666">-</span> np<span style="color: #666666">.</span>mean(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>))<span style="color: #666666">**2</span> )
|
||||
variance[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>var(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>) )
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Polynomial degree:'</span>, degree)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Error:'</span>, error[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Bias^2:'</span>, bias[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Var:'</span>, variance[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'{} >= {} + {} = {}'</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))
|
||||
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, error, label<span style="color: #666666">=</span><span style="color: #BA2121">'Error'</span>)
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, bias, label<span style="color: #666666">=</span><span style="color: #BA2121">'bias'</span>)
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, variance, label<span style="color: #666666">=</span><span style="color: #BA2121">'Variance'</span>)
|
||||
plt<span style="color: #666666">.</span>legend()
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>bag_clf<span style="color: #666666">.</span>fit(X_train, y_train)
|
||||
y_pred <span style="color: #666666">=</span> bag_clf<span style="color: #666666">.</span>predict(X_test)
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> RandomForestClassifier
|
||||
rnd_clf <span style="color: #666666">=</span> RandomForestClassifier(n_estimators<span style="color: #666666">=500</span>, max_leaf_nodes<span style="color: #666666">=16</span>, n_jobs<span style="color: #666666">=-1</span>, random_state<span style="color: #666666">=42</span>)
|
||||
rnd_clf<span style="color: #666666">.</span>fit(X_train, y_train)
|
||||
y_pred_rf <span style="color: #666666">=</span> rnd_clf<span style="color: #666666">.</span>predict(X_test)
|
||||
np<span style="color: #666666">.</span>sum(y_pred <span style="color: #666666">==</span> y_pred_rf) <span style="color: #666666">/</span> <span style="color: #008000">len</span>(y_pred)
|
||||
</pre></div>
|
||||
<p>
|
||||
<p>
|
||||
@@ -323,7 +285,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs053.html">54</a></li>
|
||||
<li><a href="._DecisionTrees-bs054.html">55</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs046.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -240,20 +240,63 @@ MathJax.Hub.Config({
|
||||
<a name="part0046"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec45" class="anchor">Boosting, a Bird'e Eye </h2>
|
||||
<h2 id="___sec45" class="anchor">Bootstrap with Random Forests Instead of a Single Tree, own Bagging </h2>
|
||||
|
||||
<p>
|
||||
The basic idea is to combine weak classifiers in order to create a good
|
||||
classifier. With a weak classifier we often intend a classifier which
|
||||
produces results which are only slightly better than we would get by
|
||||
random guesses.
|
||||
|
||||
<p>
|
||||
This is done by applying in an iterative way a weak (or a standard
|
||||
classifier like decision trees) to modify the data. In each iteration
|
||||
we emphasize those observations which are misclassified by weighting
|
||||
them with a factor.
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
|
||||
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.pipeline</span> <span style="color: #008000; font-weight: bold">import</span> make_pipeline
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.utils</span> <span style="color: #008000; font-weight: bold">import</span> resample
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> RandomForestRegressor
|
||||
|
||||
np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>seed(<span style="color: #666666">2018</span>)
|
||||
|
||||
n <span style="color: #666666">=</span> <span style="color: #666666">100</span>
|
||||
n_boostraps <span style="color: #666666">=</span> <span style="color: #666666">100</span>
|
||||
maxdegree <span style="color: #666666">=</span> <span style="color: #666666">14</span>
|
||||
|
||||
<span style="color: #408080; font-style: italic"># Make data set.</span>
|
||||
x <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linspace(<span style="color: #666666">-3</span>, <span style="color: #666666">3</span>, n)<span style="color: #666666">.</span>reshape(<span style="color: #666666">-1</span>, <span style="color: #666666">1</span>)
|
||||
y <span style="color: #666666">=</span> np<span style="color: #666666">.</span>exp(<span style="color: #666666">-</span>x<span style="color: #666666">**2</span>) <span style="color: #666666">+</span> <span style="color: #666666">1.5</span> <span style="color: #666666">*</span> np<span style="color: #666666">.</span>exp(<span style="color: #666666">-</span>(x<span style="color: #666666">-2</span>)<span style="color: #666666">**2</span>)<span style="color: #666666">+</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>normal(<span style="color: #666666">0</span>, <span style="color: #666666">0.1</span>, x<span style="color: #666666">.</span>shape)
|
||||
error <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdegree)
|
||||
bias <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdegree)
|
||||
variance <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdegree)
|
||||
polydegree <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdegree)
|
||||
X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(x, y, test_size<span style="color: #666666">=0.2</span>)
|
||||
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> StandardScaler
|
||||
scaler <span style="color: #666666">=</span> StandardScaler()
|
||||
scaler<span style="color: #666666">.</span>fit(X_train)
|
||||
X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_train)
|
||||
X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
|
||||
|
||||
<span style="color: #008000; font-weight: bold">for</span> degree <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(maxdegree):
|
||||
model <span style="color: #666666">=</span> RandomForestRegressor()
|
||||
y_pred <span style="color: #666666">=</span> np<span style="color: #666666">.</span>empty((y_test<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>], n_boostraps))
|
||||
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(n_boostraps):
|
||||
x_, y_ <span style="color: #666666">=</span> resample(X_train_scaled, y_train)
|
||||
model<span style="color: #666666">.</span>fit(x_, y_<span style="color: #666666">.</span>ravel())
|
||||
y_pred[:, i] <span style="color: #666666">=</span> model<span style="color: #666666">.</span>predict(X_test_scaled)<span style="color: #666666">.</span>ravel()
|
||||
|
||||
polydegree[degree] <span style="color: #666666">=</span> degree
|
||||
error[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> y_pred)<span style="color: #666666">**2</span>, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>) )
|
||||
bias[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( (y_test <span style="color: #666666">-</span> np<span style="color: #666666">.</span>mean(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>))<span style="color: #666666">**2</span> )
|
||||
variance[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>var(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>) )
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Polynomial degree:'</span>, degree)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Error:'</span>, error[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Bias^2:'</span>, bias[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Var:'</span>, variance[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'{} >= {} + {} = {}'</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))
|
||||
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, error, label<span style="color: #666666">=</span><span style="color: #BA2121">'Error'</span>)
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, bias, label<span style="color: #666666">=</span><span style="color: #BA2121">'bias'</span>)
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, variance, label<span style="color: #666666">=</span><span style="color: #BA2121">'Variance'</span>)
|
||||
plt<span style="color: #666666">.</span>legend()
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
</pre></div>
|
||||
<p>
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
@@ -280,7 +323,7 @@ them with a factor.
|
||||
<li><a href="._DecisionTrees-bs054.html">55</a></li>
|
||||
<li><a href="._DecisionTrees-bs055.html">56</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs047.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -240,24 +240,19 @@ MathJax.Hub.Config({
|
||||
<a name="part0047"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec46" class="anchor">Adaptive boosting: AdaBoost, Basic Algorithm </h2>
|
||||
<h2 id="___sec46" class="anchor">Boosting, a Bird'e Eye </h2>
|
||||
|
||||
<p>
|
||||
The algorithm here is rather straightforward. Assume that our weak
|
||||
classifier is a decision tree and we consider a binary set of outputs
|
||||
with \( y_i \in \{-1,1\} \) and \( i=0,1,2,\dots,n-1 \) as our set of
|
||||
observations. Our design matrix is given in terms of the
|
||||
feature/predictor vectors
|
||||
\( \boldsymbol{X}=[\boldsymbol{x}_0\boldsymbol{x}_1\dots\boldsymbol{x}_{p-1} \). Finally, we define also a
|
||||
classifier determined by our data via a function \( G(\boldsymbol{X}) \). This function tells us how well we are able to classify our outputs/targets \( \boldsymbol{y} \).
|
||||
The basic idea is to combine weak classifiers in order to create a good
|
||||
classifier. With a weak classifier we often intend a classifier which
|
||||
produces results which are only slightly better than we would get by
|
||||
random guesses.
|
||||
|
||||
<p>
|
||||
We can then define the misclassification error \( \mathrm{err} \) as
|
||||
$$
|
||||
\mathrm{err}=\frac{1}{n}\sum_{i=0}^{n-1}I(y_i\ne G(\boldsymbol{X}_{i*}),
|
||||
$$
|
||||
|
||||
where the function \( I() \) is one if we misclassify and zero if we classify correctly.
|
||||
This is done by applying in an iterative way a weak (or a standard
|
||||
classifier like decision trees) to modify the data. In each iteration
|
||||
we emphasize those observations which are misclassified by weighting
|
||||
them with a factor.
|
||||
|
||||
<p>
|
||||
<p>
|
||||
@@ -284,6 +279,8 @@ where the function \( I() \) is one if we misclassify and zero if we classify co
|
||||
<li><a href="._DecisionTrees-bs054.html">55</a></li>
|
||||
<li><a href="._DecisionTrees-bs055.html">56</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs048.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -240,42 +240,24 @@ MathJax.Hub.Config({
|
||||
<a name="part0048"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec47" class="anchor">Basic Steps of AdaBoost </h2>
|
||||
<h2 id="___sec47" class="anchor">Adaptive boosting: AdaBoost, Basic Algorithm </h2>
|
||||
|
||||
<p>
|
||||
With the above definitions we are now ready to set up the algorithm for AdaBoost.
|
||||
The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.
|
||||
|
||||
<ol>
|
||||
<li> We start by initializing all weights to \( w_i = 1/n \), with \( i=0,1,2,\dots n-1 \). It is to see then that \( \sum_{i=0}^{n-1}w_i = 1 \).</li>
|
||||
<li> We rewrite the misclassification error as</li>
|
||||
</ol>
|
||||
The algorithm here is rather straightforward. Assume that our weak
|
||||
classifier is a decision tree and we consider a binary set of outputs
|
||||
with \( y_i \in \{-1,1\} \) and \( i=0,1,2,\dots,n-1 \) as our set of
|
||||
observations. Our design matrix is given in terms of the
|
||||
feature/predictor vectors
|
||||
\( \boldsymbol{X}=[\boldsymbol{x}_0\boldsymbol{x}_1\dots\boldsymbol{x}_{p-1} \). Finally, we define also a
|
||||
classifier determined by our data via a function \( G(\boldsymbol{X}) \). This function tells us how well we are able to classify our outputs/targets \( \boldsymbol{y} \).
|
||||
|
||||
<p>
|
||||
We can then define the misclassification error \( \mathrm{err} \) as
|
||||
$$
|
||||
\mathrm{err}=\frac{\sum_{i=0}^{n-1}w_iI(y_i\ne G(\boldsymbol{X}_{i*})}{\sum_{i=0}^{n-1}w_i},
|
||||
\mathrm{err}=\frac{1}{n}\sum_{i=0}^{n-1}I(y_i\ne G(\boldsymbol{X}_{i*}),
|
||||
$$
|
||||
|
||||
|
||||
<ol>
|
||||
<li> Then we start looping over all attempts at classifying, namely we start an iterative process for \( m=1:M \), where \( M \) is the final number of classifications. Our given classifier could for example be a plain decision tree.
|
||||
|
||||
<ol type="a"></li>
|
||||
<li> Fit thus a given classifier to the training using the weights \( w_i \).</li>
|
||||
<li> Compute then \( \mathrm{err} \) and figure out which events are classified properly and which are classified wrongly.</li>
|
||||
<li> Define a quantity $\alpha_{m} = \log{(1-\mathrm{err})/\mathrm{err}}</li>
|
||||
<li> Set the new weights to $w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*})}.</li>
|
||||
</ol>
|
||||
|
||||
<li> Compute the new classifier $G(\boldsymbol{X})= \sum_{i=0}^{n-1}\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*}).</li>
|
||||
</ol>
|
||||
|
||||
For the iterations with \( m \le 2 \) the weights are modified
|
||||
individually at each steps. The obersvations which were misclassified
|
||||
at iteration \( m-1 \) have a weight which is larger than those which were
|
||||
classified properly. As this proceeds, the observations which were
|
||||
difficult to classifiy correctly are given a larger influence. Each
|
||||
new classification step \( m \) is then forced to concentrate on those
|
||||
observations that are missed in the previous iterations.
|
||||
where the function \( I() \) is one if we misclassify and zero if we classify correctly.
|
||||
|
||||
<p>
|
||||
<p>
|
||||
@@ -301,6 +283,7 @@ observations that are missed in the previous iterations.
|
||||
<li><a href="._DecisionTrees-bs054.html">55</a></li>
|
||||
<li><a href="._DecisionTrees-bs055.html">56</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs049.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -240,7 +240,42 @@ MathJax.Hub.Config({
|
||||
<a name="part0049"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec48" class="anchor">Figure to Illustrate the Iterative Classification Process </h2>
|
||||
<h2 id="___sec48" class="anchor">Basic Steps of AdaBoost </h2>
|
||||
|
||||
<p>
|
||||
With the above definitions we are now ready to set up the algorithm for AdaBoost.
|
||||
The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.
|
||||
|
||||
<ol>
|
||||
<li> We start by initializing all weights to \( w_i = 1/n \), with \( i=0,1,2,\dots n-1 \). It is to see then that \( \sum_{i=0}^{n-1}w_i = 1 \).</li>
|
||||
<li> We rewrite the misclassification error as</li>
|
||||
</ol>
|
||||
|
||||
$$
|
||||
\mathrm{err}=\frac{\sum_{i=0}^{n-1}w_iI(y_i\ne G(\boldsymbol{X}_{i*})}{\sum_{i=0}^{n-1}w_i},
|
||||
$$
|
||||
|
||||
|
||||
<ol>
|
||||
<li> Then we start looping over all attempts at classifying, namely we start an iterative process for \( m=1:M \), where \( M \) is the final number of classifications. Our given classifier could for example be a plain decision tree.
|
||||
|
||||
<ol type="a"></li>
|
||||
<li> Fit then a given classifier to the training using the weights \( w_i \).</li>
|
||||
<li> Compute then \( \mathrm{err} \) and figure out which events are classified properly and which are classified wrongly.</li>
|
||||
<li> Define a quantity $\alpha_{m} = \log{(1-\mathrm{err})/\mathrm{err}}</li>
|
||||
<li> Set the new weights to $w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*})}.</li>
|
||||
</ol>
|
||||
|
||||
<li> Compute the new classifier $G(\boldsymbol{X})= \sum_{i=0}^{n-1}\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*}).</li>
|
||||
</ol>
|
||||
|
||||
For the iterations with \( m \le 2 \) the weights are modified
|
||||
individually at each steps. The obersvations which were misclassified
|
||||
at iteration \( m-1 \) have a weight which is larger than those which were
|
||||
classified properly. As this proceeds, the observations which were
|
||||
difficult to classifiy correctly are given a larger influence. Each
|
||||
new classification step \( m \) is then forced to concentrate on those
|
||||
observations that are missed in the previous iterations.
|
||||
|
||||
<p>
|
||||
<p>
|
||||
@@ -265,6 +300,7 @@ MathJax.Hub.Config({
|
||||
<li><a href="._DecisionTrees-bs054.html">55</a></li>
|
||||
<li><a href="._DecisionTrees-bs055.html">56</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs050.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -302,6 +302,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs054.html">55</a></li>
|
||||
<li><a href="._DecisionTrees-bs055.html">56</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs051.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -273,6 +273,7 @@ function was the least squares function.
|
||||
<li><a href="._DecisionTrees-bs054.html">55</a></li>
|
||||
<li><a href="._DecisionTrees-bs055.html">56</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs052.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
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|
||||
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|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -283,6 +283,7 @@ The way we proceed in an iterative fashion is to
|
||||
<li><a href="._DecisionTrees-bs054.html">55</a></li>
|
||||
<li><a href="._DecisionTrees-bs055.html">56</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs053.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
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|
||||
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|
||||
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|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
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|
||||
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|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
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|
||||
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|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
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|
||||
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|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
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|
||||
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|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
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|
||||
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|
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|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -346,6 +346,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs054.html">55</a></li>
|
||||
<li><a href="._DecisionTrees-bs055.html">56</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs054.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
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('Random forests', 2, None, '___sec40'),
|
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('Random Forest Algorithm', 2, None, '___sec41'),
|
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|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
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|
||||
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|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
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|
||||
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|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
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|
||||
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|
||||
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||||
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('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
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||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
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('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Changing the Level of the Decision Tree</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree, own Bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -283,7 +283,7 @@ MathJax.Hub.Config({
|
||||
<li><a href="._DecisionTrees-bs008.html">9</a></li>
|
||||
<li><a href="._DecisionTrees-bs009.html">10</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs001.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -1777,7 +1777,67 @@ plt.show()
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec40">Random forests </h2>
|
||||
<h2 id="___sec40">Changing the Level of the Decision Tree </h2>
|
||||
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
|
||||
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.pipeline</span> <span style="color: #8B008B; font-weight: bold">import</span> make_pipeline
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.utils</span> <span style="color: #8B008B; font-weight: bold">import</span> resample
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeRegressor
|
||||
|
||||
n = <span style="color: #B452CD">100</span>
|
||||
n_boostraps = <span style="color: #B452CD">100</span>
|
||||
maxdepth = <span style="color: #B452CD">8</span>
|
||||
|
||||
<span style="color: #228B22"># Make data set.</span>
|
||||
x = np.linspace(-<span style="color: #B452CD">3</span>, <span style="color: #B452CD">3</span>, n).reshape(-<span style="color: #B452CD">1</span>, <span style="color: #B452CD">1</span>)
|
||||
y = np.exp(-x**<span style="color: #B452CD">2</span>) + <span style="color: #B452CD">1.5</span> * np.exp(-(x-<span style="color: #B452CD">2</span>)**<span style="color: #B452CD">2</span>)+ np.random.normal(<span style="color: #B452CD">0</span>, <span style="color: #B452CD">0.1</span>, x.shape)
|
||||
error = np.zeros(maxdepth)
|
||||
bias = np.zeros(maxdepth)
|
||||
variance = np.zeros(maxdepth)
|
||||
polydegree = np.zeros(maxdepth)
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=<span style="color: #B452CD">0.2</span>)
|
||||
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> StandardScaler
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
|
||||
<span style="color: #8B008B; font-weight: bold">for</span> degree <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(<span style="color: #B452CD">1</span>,maxdepth):
|
||||
model = DecisionTreeRegressor(max_depth=degree)
|
||||
y_pred = np.empty((y_test.shape[<span style="color: #B452CD">0</span>], n_boostraps))
|
||||
<span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(n_boostraps):
|
||||
x_, y_ = resample(X_train_scaled, y_train)
|
||||
model.fit(x_, y_)
|
||||
y_pred[:, i] = model.predict(X_test_scaled)<span style="color: #228B22">#.ravel()</span>
|
||||
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**<span style="color: #B452CD">2</span>, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #658b00">True</span>) )
|
||||
bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #658b00">True</span>))**<span style="color: #B452CD">2</span> )
|
||||
variance[degree] = np.mean( np.var(y_pred, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #658b00">True</span>) )
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Polynomial degree:'</span>, degree)
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Error:'</span>, error[degree])
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Bias^2:'</span>, bias[degree])
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Var:'</span>, variance[degree])
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'{} >= {} + {} = {}'</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
|
||||
|
||||
plt.xlim(<span style="color: #B452CD">1</span>,maxdepth)
|
||||
plt.plot(polydegree, error, label=<span style="color: #CD5555">'Error'</span>)
|
||||
plt.plot(polydegree, bias, label=<span style="color: #CD5555">'bias'</span>)
|
||||
plt.plot(polydegree, variance, label=<span style="color: #CD5555">'Variance'</span>)
|
||||
plt.legend()
|
||||
plt.show()
|
||||
</pre></div>
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec41">Random forests </h2>
|
||||
|
||||
<p>
|
||||
Random forests provide an improvement over bagged trees by way of a
|
||||
@@ -1823,7 +1883,7 @@ this setting.
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec41">Random Forest Algorithm </h2>
|
||||
<h2 id="___sec42">Random Forest Algorithm </h2>
|
||||
The algorithm described here can be applied to both classification and regression problems.
|
||||
|
||||
<p>
|
||||
@@ -1854,7 +1914,7 @@ We will grow of forest of say \( M \) trees.
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec42">Random Forests Compared with other Methods on the Cancer Data </h2>
|
||||
<h2 id="___sec43">Random Forests Compared with other Methods on the Cancer Data </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
@@ -1928,7 +1988,7 @@ plt.show()
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec43">Compare Bagging on Trees with Random Forests </h2>
|
||||
<h2 id="___sec44">Compare Bagging on Trees with Random Forests </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
@@ -1951,7 +2011,7 @@ np.sum(y_pred == y_pred_rf) / <span style="color: #658b00">len</span>(y_pred)
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec44">Bootstrap with Random Forests Instead of a Single Tree </h2>
|
||||
<h2 id="___sec45">Bootstrap with Random Forests Instead of a Single Tree, own Bagging </h2>
|
||||
|
||||
<p>
|
||||
|
||||
@@ -1965,7 +2025,7 @@ np.sum(y_pred == y_pred_rf) / <span style="color: #658b00">len</span>(y_pred)
|
||||
|
||||
np.random.seed(<span style="color: #B452CD">2018</span>)
|
||||
|
||||
n = <span style="color: #B452CD">40</span>
|
||||
n = <span style="color: #B452CD">100</span>
|
||||
n_boostraps = <span style="color: #B452CD">100</span>
|
||||
maxdegree = <span style="color: #B452CD">14</span>
|
||||
|
||||
@@ -1989,7 +2049,7 @@ X_test_scaled = scaler.transform(X_test)
|
||||
y_pred = np.empty((y_test.shape[<span style="color: #B452CD">0</span>], n_boostraps))
|
||||
<span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(n_boostraps):
|
||||
x_, y_ = resample(X_train_scaled, y_train)
|
||||
model.fit(x_, y_)
|
||||
model.fit(x_, y_.ravel())
|
||||
y_pred[:, i] = model.predict(X_test_scaled).ravel()
|
||||
|
||||
polydegree[degree] = degree
|
||||
@@ -2012,7 +2072,7 @@ plt.show()
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec45">Boosting, a Bird'e Eye </h2>
|
||||
<h2 id="___sec46">Boosting, a Bird'e Eye </h2>
|
||||
|
||||
<p>
|
||||
The basic idea is to combine weak classifiers in order to create a good
|
||||
@@ -2029,7 +2089,7 @@ them with a factor.
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec46">Adaptive boosting: AdaBoost, Basic Algorithm </h2>
|
||||
<h2 id="___sec47">Adaptive boosting: AdaBoost, Basic Algorithm </h2>
|
||||
|
||||
<p>
|
||||
The algorithm here is rather straightforward. Assume that our weak
|
||||
@@ -2053,7 +2113,7 @@ where the function \( I() \) is one if we misclassify and zero if we classify co
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec47">Basic Steps of AdaBoost </h2>
|
||||
<h2 id="___sec48">Basic Steps of AdaBoost </h2>
|
||||
|
||||
<p>
|
||||
With the above definitions we are now ready to set up the algorithm for AdaBoost.
|
||||
@@ -2074,7 +2134,7 @@ $$
|
||||
<p><li> Then we start looping over all attempts at classifying, namely we start an iterative process for \( m=1:M \), where \( M \) is the final number of classifications. Our given classifier could for example be a plain decision tree.
|
||||
|
||||
<ol type="a"></li>
|
||||
<p><li> Fit thus a given classifier to the training using the weights \( w_i \).</li>
|
||||
<p><li> Fit then a given classifier to the training using the weights \( w_i \).</li>
|
||||
<p><li> Compute then \( \mathrm{err} \) and figure out which events are classified properly and which are classified wrongly.</li>
|
||||
<p><li> Define a quantity $\alpha_{m} = \log{(1-\mathrm{err})/\mathrm{err}}</li>
|
||||
<p><li> Set the new weights to $w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*})}.</li>
|
||||
@@ -2093,11 +2153,6 @@ observations that are missed in the previous iterations.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec48">Figure to Illustrate the Iterative Classification Process </h2>
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec49">AdaBoost Examples </h2>
|
||||
|
||||
@@ -2363,7 +2418,63 @@ It is now the algorithm which wins essentially all ML competitions!!!
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec55">Xgboost on the Cancer Data </h2>
|
||||
<h2 id="___sec55">Regression Case </h2>
|
||||
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
|
||||
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
|
||||
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">xgboost</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">xgb</span>
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> StandardScaler
|
||||
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">scikitplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">skplt</span>
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> mean_squared_error
|
||||
|
||||
n = <span style="color: #B452CD">40</span>
|
||||
n_boostraps = <span style="color: #B452CD">100</span>
|
||||
maxdegree = <span style="color: #B452CD">8</span>
|
||||
|
||||
<span style="color: #228B22"># Make data set.</span>
|
||||
x = np.linspace(-<span style="color: #B452CD">3</span>, <span style="color: #B452CD">3</span>, n).reshape(-<span style="color: #B452CD">1</span>, <span style="color: #B452CD">1</span>)
|
||||
y = np.exp(-x**<span style="color: #B452CD">2</span>) + <span style="color: #B452CD">1.5</span> * np.exp(-(x-<span style="color: #B452CD">2</span>)**<span style="color: #B452CD">2</span>)+ np.random.normal(<span style="color: #B452CD">0</span>, <span style="color: #B452CD">0.1</span>, x.shape)
|
||||
|
||||
error = np.zeros(maxdegree)
|
||||
bias = np.zeros(maxdegree)
|
||||
variance = np.zeros(maxdegree)
|
||||
polydegree = np.zeros(maxdegree)
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=<span style="color: #B452CD">0.2</span>)
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
|
||||
<span style="color: #8B008B; font-weight: bold">for</span> degree <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(maxdegree):
|
||||
model = xgb.XGBRegressor(objective =<span style="color: #CD5555">'reg:linear'</span>, colsample_bytree = <span style="color: #B452CD">0.3</span>, learning_rate = <span style="color: #B452CD">0.1</span>,
|
||||
max_depth = maxdegree, alpha = <span style="color: #B452CD">10</span>, n_estimators = <span style="color: #B452CD">10</span>)
|
||||
model.fit(X_train_scaled,y_train)
|
||||
y_pred = model.predict(X_test_scaled)
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**<span style="color: #B452CD">2</span>) )
|
||||
bias[degree] = np.mean( (y_test - np.mean(y_pred))**<span style="color: #B452CD">2</span> )
|
||||
variance[degree] = np.mean( np.var(y_pred) )
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Max depth:'</span>, degree)
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Error:'</span>, error[degree])
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Bias^2:'</span>, bias[degree])
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Var:'</span>, variance[degree])
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'{} >= {} + {} = {}'</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
|
||||
|
||||
plt.plot(polydegree, error, label=<span style="color: #CD5555">'Error'</span>)
|
||||
plt.plot(polydegree, bias, label=<span style="color: #CD5555">'bias'</span>)
|
||||
plt.plot(polydegree, variance, label=<span style="color: #CD5555">'Variance'</span>)
|
||||
plt.legend()
|
||||
plt.show()
|
||||
</pre></div>
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec56">Xgboost on the Cancer Data </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
|
||||
@@ -121,37 +121,36 @@ div { text-align: justify; text-justify: inter-word; }
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -1776,7 +1775,66 @@ plt.show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec40">Random forests </h2>
|
||||
<h2 id="___sec40">Changing the Level of the Decision Tree </h2>
|
||||
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
|
||||
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.pipeline</span> <span style="color: #8B008B; font-weight: bold">import</span> make_pipeline
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.utils</span> <span style="color: #8B008B; font-weight: bold">import</span> resample
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeRegressor
|
||||
|
||||
n = <span style="color: #B452CD">100</span>
|
||||
n_boostraps = <span style="color: #B452CD">100</span>
|
||||
maxdepth = <span style="color: #B452CD">8</span>
|
||||
|
||||
<span style="color: #228B22"># Make data set.</span>
|
||||
x = np.linspace(-<span style="color: #B452CD">3</span>, <span style="color: #B452CD">3</span>, n).reshape(-<span style="color: #B452CD">1</span>, <span style="color: #B452CD">1</span>)
|
||||
y = np.exp(-x**<span style="color: #B452CD">2</span>) + <span style="color: #B452CD">1.5</span> * np.exp(-(x-<span style="color: #B452CD">2</span>)**<span style="color: #B452CD">2</span>)+ np.random.normal(<span style="color: #B452CD">0</span>, <span style="color: #B452CD">0.1</span>, x.shape)
|
||||
error = np.zeros(maxdepth)
|
||||
bias = np.zeros(maxdepth)
|
||||
variance = np.zeros(maxdepth)
|
||||
polydegree = np.zeros(maxdepth)
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=<span style="color: #B452CD">0.2</span>)
|
||||
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> StandardScaler
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
|
||||
<span style="color: #8B008B; font-weight: bold">for</span> degree <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(<span style="color: #B452CD">1</span>,maxdepth):
|
||||
model = DecisionTreeRegressor(max_depth=degree)
|
||||
y_pred = np.empty((y_test.shape[<span style="color: #B452CD">0</span>], n_boostraps))
|
||||
<span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(n_boostraps):
|
||||
x_, y_ = resample(X_train_scaled, y_train)
|
||||
model.fit(x_, y_)
|
||||
y_pred[:, i] = model.predict(X_test_scaled)<span style="color: #228B22">#.ravel()</span>
|
||||
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**<span style="color: #B452CD">2</span>, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #658b00">True</span>) )
|
||||
bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #658b00">True</span>))**<span style="color: #B452CD">2</span> )
|
||||
variance[degree] = np.mean( np.var(y_pred, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #658b00">True</span>) )
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Polynomial degree:'</span>, degree)
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Error:'</span>, error[degree])
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Bias^2:'</span>, bias[degree])
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Var:'</span>, variance[degree])
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'{} >= {} + {} = {}'</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
|
||||
|
||||
plt.xlim(<span style="color: #B452CD">1</span>,maxdepth)
|
||||
plt.plot(polydegree, error, label=<span style="color: #CD5555">'Error'</span>)
|
||||
plt.plot(polydegree, bias, label=<span style="color: #CD5555">'bias'</span>)
|
||||
plt.plot(polydegree, variance, label=<span style="color: #CD5555">'Variance'</span>)
|
||||
plt.legend()
|
||||
plt.show()
|
||||
</pre></div>
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec41">Random forests </h2>
|
||||
|
||||
<p>
|
||||
Random forests provide an improvement over bagged trees by way of a
|
||||
@@ -1820,7 +1878,7 @@ this setting.
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec41">Random Forest Algorithm </h2>
|
||||
<h2 id="___sec42">Random Forest Algorithm </h2>
|
||||
The algorithm described here can be applied to both classification and regression problems.
|
||||
|
||||
<p>
|
||||
@@ -1846,7 +1904,7 @@ We will grow of forest of say \( M \) trees.
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec42">Random Forests Compared with other Methods on the Cancer Data </h2>
|
||||
<h2 id="___sec43">Random Forests Compared with other Methods on the Cancer Data </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
@@ -1919,7 +1977,7 @@ plt.show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec43">Compare Bagging on Trees with Random Forests </h2>
|
||||
<h2 id="___sec44">Compare Bagging on Trees with Random Forests </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
@@ -1941,7 +1999,7 @@ np.sum(y_pred == y_pred_rf) / <span style="color: #658b00">len</span>(y_pred)
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec44">Bootstrap with Random Forests Instead of a Single Tree </h2>
|
||||
<h2 id="___sec45">Bootstrap with Random Forests Instead of a Single Tree, own Bagging </h2>
|
||||
|
||||
<p>
|
||||
|
||||
@@ -1955,7 +2013,7 @@ np.sum(y_pred == y_pred_rf) / <span style="color: #658b00">len</span>(y_pred)
|
||||
|
||||
np.random.seed(<span style="color: #B452CD">2018</span>)
|
||||
|
||||
n = <span style="color: #B452CD">40</span>
|
||||
n = <span style="color: #B452CD">100</span>
|
||||
n_boostraps = <span style="color: #B452CD">100</span>
|
||||
maxdegree = <span style="color: #B452CD">14</span>
|
||||
|
||||
@@ -1979,7 +2037,7 @@ X_test_scaled = scaler.transform(X_test)
|
||||
y_pred = np.empty((y_test.shape[<span style="color: #B452CD">0</span>], n_boostraps))
|
||||
<span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(n_boostraps):
|
||||
x_, y_ = resample(X_train_scaled, y_train)
|
||||
model.fit(x_, y_)
|
||||
model.fit(x_, y_.ravel())
|
||||
y_pred[:, i] = model.predict(X_test_scaled).ravel()
|
||||
|
||||
polydegree[degree] = degree
|
||||
@@ -2001,7 +2059,7 @@ plt.show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec45">Boosting, a Bird'e Eye </h2>
|
||||
<h2 id="___sec46">Boosting, a Bird'e Eye </h2>
|
||||
|
||||
<p>
|
||||
The basic idea is to combine weak classifiers in order to create a good
|
||||
@@ -2018,7 +2076,7 @@ them with a factor.
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec46">Adaptive boosting: AdaBoost, Basic Algorithm </h2>
|
||||
<h2 id="___sec47">Adaptive boosting: AdaBoost, Basic Algorithm </h2>
|
||||
|
||||
<p>
|
||||
The algorithm here is rather straightforward. Assume that our weak
|
||||
@@ -2040,7 +2098,7 @@ where the function \( I() \) is one if we misclassify and zero if we classify co
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec47">Basic Steps of AdaBoost </h2>
|
||||
<h2 id="___sec48">Basic Steps of AdaBoost </h2>
|
||||
|
||||
<p>
|
||||
With the above definitions we are now ready to set up the algorithm for AdaBoost.
|
||||
@@ -2060,7 +2118,7 @@ $$
|
||||
<li> Then we start looping over all attempts at classifying, namely we start an iterative process for \( m=1:M \), where \( M \) is the final number of classifications. Our given classifier could for example be a plain decision tree.
|
||||
|
||||
<ol type="a"></li>
|
||||
<li> Fit thus a given classifier to the training using the weights \( w_i \).</li>
|
||||
<li> Fit then a given classifier to the training using the weights \( w_i \).</li>
|
||||
<li> Compute then \( \mathrm{err} \) and figure out which events are classified properly and which are classified wrongly.</li>
|
||||
<li> Define a quantity $\alpha_{m} = \log{(1-\mathrm{err})/\mathrm{err}}</li>
|
||||
<li> Set the new weights to $w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*})}.</li>
|
||||
@@ -2080,11 +2138,6 @@ observations that are missed in the previous iterations.
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec48">Figure to Illustrate the Iterative Classification Process </h2>
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec49">AdaBoost Examples </h2>
|
||||
|
||||
<p>
|
||||
@@ -2344,7 +2397,62 @@ It is now the algorithm which wins essentially all ML competitions!!!
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec55">Xgboost on the Cancer Data </h2>
|
||||
<h2 id="___sec55">Regression Case </h2>
|
||||
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
|
||||
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
|
||||
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">xgboost</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">xgb</span>
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> StandardScaler
|
||||
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">scikitplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">skplt</span>
|
||||
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> mean_squared_error
|
||||
|
||||
n = <span style="color: #B452CD">40</span>
|
||||
n_boostraps = <span style="color: #B452CD">100</span>
|
||||
maxdegree = <span style="color: #B452CD">8</span>
|
||||
|
||||
<span style="color: #228B22"># Make data set.</span>
|
||||
x = np.linspace(-<span style="color: #B452CD">3</span>, <span style="color: #B452CD">3</span>, n).reshape(-<span style="color: #B452CD">1</span>, <span style="color: #B452CD">1</span>)
|
||||
y = np.exp(-x**<span style="color: #B452CD">2</span>) + <span style="color: #B452CD">1.5</span> * np.exp(-(x-<span style="color: #B452CD">2</span>)**<span style="color: #B452CD">2</span>)+ np.random.normal(<span style="color: #B452CD">0</span>, <span style="color: #B452CD">0.1</span>, x.shape)
|
||||
|
||||
error = np.zeros(maxdegree)
|
||||
bias = np.zeros(maxdegree)
|
||||
variance = np.zeros(maxdegree)
|
||||
polydegree = np.zeros(maxdegree)
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=<span style="color: #B452CD">0.2</span>)
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
|
||||
<span style="color: #8B008B; font-weight: bold">for</span> degree <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(maxdegree):
|
||||
model = xgb.XGBRegressor(objective =<span style="color: #CD5555">'reg:linear'</span>, colsample_bytree = <span style="color: #B452CD">0.3</span>, learning_rate = <span style="color: #B452CD">0.1</span>,
|
||||
max_depth = maxdegree, alpha = <span style="color: #B452CD">10</span>, n_estimators = <span style="color: #B452CD">10</span>)
|
||||
model.fit(X_train_scaled,y_train)
|
||||
y_pred = model.predict(X_test_scaled)
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**<span style="color: #B452CD">2</span>) )
|
||||
bias[degree] = np.mean( (y_test - np.mean(y_pred))**<span style="color: #B452CD">2</span> )
|
||||
variance[degree] = np.mean( np.var(y_pred) )
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Max depth:'</span>, degree)
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Error:'</span>, error[degree])
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Bias^2:'</span>, bias[degree])
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'Var:'</span>, variance[degree])
|
||||
<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">'{} >= {} + {} = {}'</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
|
||||
|
||||
plt.plot(polydegree, error, label=<span style="color: #CD5555">'Error'</span>)
|
||||
plt.plot(polydegree, bias, label=<span style="color: #CD5555">'bias'</span>)
|
||||
plt.plot(polydegree, variance, label=<span style="color: #CD5555">'Variance'</span>)
|
||||
plt.legend()
|
||||
plt.show()
|
||||
</pre></div>
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec56">Xgboost on the Cancer Data </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
|
||||
@@ -126,37 +126,36 @@ div { text-align: justify; text-justify: inter-word; }
|
||||
'___sec37'),
|
||||
('Now Bagging', 2, None, '___sec38'),
|
||||
('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
|
||||
('Random forests', 2, None, '___sec40'),
|
||||
('Random Forest Algorithm', 2, None, '___sec41'),
|
||||
('Changing the Level of the Decision Tree', 2, None, '___sec40'),
|
||||
('Random forests', 2, None, '___sec41'),
|
||||
('Random Forest Algorithm', 2, None, '___sec42'),
|
||||
('Random Forests Compared with other Methods on the Cancer Data',
|
||||
2,
|
||||
None,
|
||||
'___sec42'),
|
||||
'___sec43'),
|
||||
('Compare Bagging on Trees with Random Forests',
|
||||
2,
|
||||
None,
|
||||
'___sec43'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree',
|
||||
'___sec44'),
|
||||
('Bootstrap with Random Forests Instead of a Single Tree, own '
|
||||
'Bagging',
|
||||
2,
|
||||
None,
|
||||
'___sec44'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec45'),
|
||||
'___sec45'),
|
||||
("Boosting, a Bird'e Eye", 2, None, '___sec46'),
|
||||
('Adaptive boosting: AdaBoost, Basic Algorithm',
|
||||
2,
|
||||
None,
|
||||
'___sec46'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec47'),
|
||||
('Figure to Illustrate the Iterative Classification Process',
|
||||
2,
|
||||
None,
|
||||
'___sec48'),
|
||||
'___sec47'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec48'),
|
||||
('AdaBoost Examples', 2, None, '___sec49'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec50'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec51'),
|
||||
('Gradient Boosting, Examples', 2, None, '___sec52'),
|
||||
('Gradient Boots with Early Stopping', 2, None, '___sec53'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec55')]}
|
||||
('Regression Case', 2, None, '___sec55'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec56')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -1781,7 +1780,66 @@ plt<span style="color: #666666">.</span>show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec40">Random forests </h2>
|
||||
<h2 id="___sec40">Changing the Level of the Decision Tree </h2>
|
||||
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
|
||||
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.pipeline</span> <span style="color: #008000; font-weight: bold">import</span> make_pipeline
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.utils</span> <span style="color: #008000; font-weight: bold">import</span> resample
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeRegressor
|
||||
|
||||
n <span style="color: #666666">=</span> <span style="color: #666666">100</span>
|
||||
n_boostraps <span style="color: #666666">=</span> <span style="color: #666666">100</span>
|
||||
maxdepth <span style="color: #666666">=</span> <span style="color: #666666">8</span>
|
||||
|
||||
<span style="color: #408080; font-style: italic"># Make data set.</span>
|
||||
x <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linspace(<span style="color: #666666">-3</span>, <span style="color: #666666">3</span>, n)<span style="color: #666666">.</span>reshape(<span style="color: #666666">-1</span>, <span style="color: #666666">1</span>)
|
||||
y <span style="color: #666666">=</span> np<span style="color: #666666">.</span>exp(<span style="color: #666666">-</span>x<span style="color: #666666">**2</span>) <span style="color: #666666">+</span> <span style="color: #666666">1.5</span> <span style="color: #666666">*</span> np<span style="color: #666666">.</span>exp(<span style="color: #666666">-</span>(x<span style="color: #666666">-2</span>)<span style="color: #666666">**2</span>)<span style="color: #666666">+</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>normal(<span style="color: #666666">0</span>, <span style="color: #666666">0.1</span>, x<span style="color: #666666">.</span>shape)
|
||||
error <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdepth)
|
||||
bias <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdepth)
|
||||
variance <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdepth)
|
||||
polydegree <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdepth)
|
||||
X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(x, y, test_size<span style="color: #666666">=0.2</span>)
|
||||
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> StandardScaler
|
||||
scaler <span style="color: #666666">=</span> StandardScaler()
|
||||
scaler<span style="color: #666666">.</span>fit(X_train)
|
||||
X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_train)
|
||||
X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
|
||||
|
||||
<span style="color: #008000; font-weight: bold">for</span> degree <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #666666">1</span>,maxdepth):
|
||||
model <span style="color: #666666">=</span> DecisionTreeRegressor(max_depth<span style="color: #666666">=</span>degree)
|
||||
y_pred <span style="color: #666666">=</span> np<span style="color: #666666">.</span>empty((y_test<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>], n_boostraps))
|
||||
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(n_boostraps):
|
||||
x_, y_ <span style="color: #666666">=</span> resample(X_train_scaled, y_train)
|
||||
model<span style="color: #666666">.</span>fit(x_, y_)
|
||||
y_pred[:, i] <span style="color: #666666">=</span> model<span style="color: #666666">.</span>predict(X_test_scaled)<span style="color: #408080; font-style: italic">#.ravel()</span>
|
||||
|
||||
polydegree[degree] <span style="color: #666666">=</span> degree
|
||||
error[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> y_pred)<span style="color: #666666">**2</span>, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>) )
|
||||
bias[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( (y_test <span style="color: #666666">-</span> np<span style="color: #666666">.</span>mean(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>))<span style="color: #666666">**2</span> )
|
||||
variance[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>var(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>) )
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Polynomial degree:'</span>, degree)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Error:'</span>, error[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Bias^2:'</span>, bias[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Var:'</span>, variance[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'{} >= {} + {} = {}'</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))
|
||||
|
||||
plt<span style="color: #666666">.</span>xlim(<span style="color: #666666">1</span>,maxdepth)
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, error, label<span style="color: #666666">=</span><span style="color: #BA2121">'Error'</span>)
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, bias, label<span style="color: #666666">=</span><span style="color: #BA2121">'bias'</span>)
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, variance, label<span style="color: #666666">=</span><span style="color: #BA2121">'Variance'</span>)
|
||||
plt<span style="color: #666666">.</span>legend()
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
</pre></div>
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec41">Random forests </h2>
|
||||
|
||||
<p>
|
||||
Random forests provide an improvement over bagged trees by way of a
|
||||
@@ -1825,7 +1883,7 @@ this setting.
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec41">Random Forest Algorithm </h2>
|
||||
<h2 id="___sec42">Random Forest Algorithm </h2>
|
||||
The algorithm described here can be applied to both classification and regression problems.
|
||||
|
||||
<p>
|
||||
@@ -1851,7 +1909,7 @@ We will grow of forest of say \( M \) trees.
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec42">Random Forests Compared with other Methods on the Cancer Data </h2>
|
||||
<h2 id="___sec43">Random Forests Compared with other Methods on the Cancer Data </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
@@ -1924,7 +1982,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec43">Compare Bagging on Trees with Random Forests </h2>
|
||||
<h2 id="___sec44">Compare Bagging on Trees with Random Forests </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
@@ -1946,7 +2004,7 @@ np<span style="color: #666666">.</span>sum(y_pred <span style="color: #666666">=
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec44">Bootstrap with Random Forests Instead of a Single Tree </h2>
|
||||
<h2 id="___sec45">Bootstrap with Random Forests Instead of a Single Tree, own Bagging </h2>
|
||||
|
||||
<p>
|
||||
|
||||
@@ -1960,7 +2018,7 @@ np<span style="color: #666666">.</span>sum(y_pred <span style="color: #666666">=
|
||||
|
||||
np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>seed(<span style="color: #666666">2018</span>)
|
||||
|
||||
n <span style="color: #666666">=</span> <span style="color: #666666">40</span>
|
||||
n <span style="color: #666666">=</span> <span style="color: #666666">100</span>
|
||||
n_boostraps <span style="color: #666666">=</span> <span style="color: #666666">100</span>
|
||||
maxdegree <span style="color: #666666">=</span> <span style="color: #666666">14</span>
|
||||
|
||||
@@ -1984,7 +2042,7 @@ X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #6
|
||||
y_pred <span style="color: #666666">=</span> np<span style="color: #666666">.</span>empty((y_test<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>], n_boostraps))
|
||||
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(n_boostraps):
|
||||
x_, y_ <span style="color: #666666">=</span> resample(X_train_scaled, y_train)
|
||||
model<span style="color: #666666">.</span>fit(x_, y_)
|
||||
model<span style="color: #666666">.</span>fit(x_, y_<span style="color: #666666">.</span>ravel())
|
||||
y_pred[:, i] <span style="color: #666666">=</span> model<span style="color: #666666">.</span>predict(X_test_scaled)<span style="color: #666666">.</span>ravel()
|
||||
|
||||
polydegree[degree] <span style="color: #666666">=</span> degree
|
||||
@@ -2006,7 +2064,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec45">Boosting, a Bird'e Eye </h2>
|
||||
<h2 id="___sec46">Boosting, a Bird'e Eye </h2>
|
||||
|
||||
<p>
|
||||
The basic idea is to combine weak classifiers in order to create a good
|
||||
@@ -2023,7 +2081,7 @@ them with a factor.
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec46">Adaptive boosting: AdaBoost, Basic Algorithm </h2>
|
||||
<h2 id="___sec47">Adaptive boosting: AdaBoost, Basic Algorithm </h2>
|
||||
|
||||
<p>
|
||||
The algorithm here is rather straightforward. Assume that our weak
|
||||
@@ -2045,7 +2103,7 @@ where the function \( I() \) is one if we misclassify and zero if we classify co
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec47">Basic Steps of AdaBoost </h2>
|
||||
<h2 id="___sec48">Basic Steps of AdaBoost </h2>
|
||||
|
||||
<p>
|
||||
With the above definitions we are now ready to set up the algorithm for AdaBoost.
|
||||
@@ -2065,7 +2123,7 @@ $$
|
||||
<li> Then we start looping over all attempts at classifying, namely we start an iterative process for \( m=1:M \), where \( M \) is the final number of classifications. Our given classifier could for example be a plain decision tree.
|
||||
|
||||
<ol type="a"></li>
|
||||
<li> Fit thus a given classifier to the training using the weights \( w_i \).</li>
|
||||
<li> Fit then a given classifier to the training using the weights \( w_i \).</li>
|
||||
<li> Compute then \( \mathrm{err} \) and figure out which events are classified properly and which are classified wrongly.</li>
|
||||
<li> Define a quantity $\alpha_{m} = \log{(1-\mathrm{err})/\mathrm{err}}</li>
|
||||
<li> Set the new weights to $w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*})}.</li>
|
||||
@@ -2085,11 +2143,6 @@ observations that are missed in the previous iterations.
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec48">Figure to Illustrate the Iterative Classification Process </h2>
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec49">AdaBoost Examples </h2>
|
||||
|
||||
<p>
|
||||
@@ -2349,7 +2402,62 @@ It is now the algorithm which wins essentially all ML competitions!!!
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec55">Xgboost on the Cancer Data </h2>
|
||||
<h2 id="___sec55">Regression Case </h2>
|
||||
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
|
||||
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
|
||||
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">xgboost</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">xgb</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> StandardScaler
|
||||
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">scikitplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">skplt</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.metrics</span> <span style="color: #008000; font-weight: bold">import</span> mean_squared_error
|
||||
|
||||
n <span style="color: #666666">=</span> <span style="color: #666666">40</span>
|
||||
n_boostraps <span style="color: #666666">=</span> <span style="color: #666666">100</span>
|
||||
maxdegree <span style="color: #666666">=</span> <span style="color: #666666">8</span>
|
||||
|
||||
<span style="color: #408080; font-style: italic"># Make data set.</span>
|
||||
x <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linspace(<span style="color: #666666">-3</span>, <span style="color: #666666">3</span>, n)<span style="color: #666666">.</span>reshape(<span style="color: #666666">-1</span>, <span style="color: #666666">1</span>)
|
||||
y <span style="color: #666666">=</span> np<span style="color: #666666">.</span>exp(<span style="color: #666666">-</span>x<span style="color: #666666">**2</span>) <span style="color: #666666">+</span> <span style="color: #666666">1.5</span> <span style="color: #666666">*</span> np<span style="color: #666666">.</span>exp(<span style="color: #666666">-</span>(x<span style="color: #666666">-2</span>)<span style="color: #666666">**2</span>)<span style="color: #666666">+</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>normal(<span style="color: #666666">0</span>, <span style="color: #666666">0.1</span>, x<span style="color: #666666">.</span>shape)
|
||||
|
||||
error <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdegree)
|
||||
bias <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdegree)
|
||||
variance <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdegree)
|
||||
polydegree <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(maxdegree)
|
||||
X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(x, y, test_size<span style="color: #666666">=0.2</span>)
|
||||
scaler <span style="color: #666666">=</span> StandardScaler()
|
||||
scaler<span style="color: #666666">.</span>fit(X_train)
|
||||
X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_train)
|
||||
X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
|
||||
|
||||
<span style="color: #008000; font-weight: bold">for</span> degree <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(maxdegree):
|
||||
model <span style="color: #666666">=</span> xgb<span style="color: #666666">.</span>XGBRegressor(objective <span style="color: #666666">=</span><span style="color: #BA2121">'reg:linear'</span>, colsample_bytree <span style="color: #666666">=</span> <span style="color: #666666">0.3</span>, learning_rate <span style="color: #666666">=</span> <span style="color: #666666">0.1</span>,
|
||||
max_depth <span style="color: #666666">=</span> maxdegree, alpha <span style="color: #666666">=</span> <span style="color: #666666">10</span>, n_estimators <span style="color: #666666">=</span> <span style="color: #666666">10</span>)
|
||||
model<span style="color: #666666">.</span>fit(X_train_scaled,y_train)
|
||||
y_pred <span style="color: #666666">=</span> model<span style="color: #666666">.</span>predict(X_test_scaled)
|
||||
polydegree[degree] <span style="color: #666666">=</span> degree
|
||||
error[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> y_pred)<span style="color: #666666">**2</span>) )
|
||||
bias[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( (y_test <span style="color: #666666">-</span> np<span style="color: #666666">.</span>mean(y_pred))<span style="color: #666666">**2</span> )
|
||||
variance[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>var(y_pred) )
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Max depth:'</span>, degree)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Error:'</span>, error[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Bias^2:'</span>, bias[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'Var:'</span>, variance[degree])
|
||||
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">'{} >= {} + {} = {}'</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))
|
||||
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, error, label<span style="color: #666666">=</span><span style="color: #BA2121">'Error'</span>)
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, bias, label<span style="color: #666666">=</span><span style="color: #BA2121">'bias'</span>)
|
||||
plt<span style="color: #666666">.</span>plot(polydegree, variance, label<span style="color: #666666">=</span><span style="color: #BA2121">'Variance'</span>)
|
||||
plt<span style="color: #666666">.</span>legend()
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
</pre></div>
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec56">Xgboost on the Cancer Data </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
|
||||
@@ -1796,6 +1796,74 @@
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Changing the Level of the Decision Tree"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from sklearn.pipeline import make_pipeline\n",
|
||||
"from sklearn.utils import resample\n",
|
||||
"from sklearn.tree import DecisionTreeRegressor\n",
|
||||
"\n",
|
||||
"n = 100\n",
|
||||
"n_boostraps = 100\n",
|
||||
"maxdepth = 8\n",
|
||||
"\n",
|
||||
"# Make data set.\n",
|
||||
"x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
|
||||
"y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n",
|
||||
"error = np.zeros(maxdepth)\n",
|
||||
"bias = np.zeros(maxdepth)\n",
|
||||
"variance = np.zeros(maxdepth)\n",
|
||||
"polydegree = np.zeros(maxdepth)\n",
|
||||
"X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
|
||||
"\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"scaler = StandardScaler()\n",
|
||||
"scaler.fit(X_train)\n",
|
||||
"X_train_scaled = scaler.transform(X_train)\n",
|
||||
"X_test_scaled = scaler.transform(X_test)\n",
|
||||
"\n",
|
||||
"for degree in range(1,maxdepth):\n",
|
||||
" model = DecisionTreeRegressor(max_depth=degree) \n",
|
||||
" y_pred = np.empty((y_test.shape[0], n_boostraps))\n",
|
||||
" for i in range(n_boostraps):\n",
|
||||
" x_, y_ = resample(X_train_scaled, y_train)\n",
|
||||
" model.fit(x_, y_)\n",
|
||||
" y_pred[:, i] = model.predict(X_test_scaled)#.ravel()\n",
|
||||
"\n",
|
||||
" polydegree[degree] = degree\n",
|
||||
" error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )\n",
|
||||
" bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )\n",
|
||||
" variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )\n",
|
||||
" print('Polynomial degree:', degree)\n",
|
||||
" print('Error:', error[degree])\n",
|
||||
" print('Bias^2:', bias[degree])\n",
|
||||
" print('Var:', variance[degree])\n",
|
||||
" print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n",
|
||||
"\n",
|
||||
"plt.xlim(1,maxdepth)\n",
|
||||
"plt.plot(polydegree, error, label='Error')\n",
|
||||
"plt.plot(polydegree, bias, label='bias')\n",
|
||||
"plt.plot(polydegree, variance, label='Variance')\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -1872,7 +1940,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 26,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -1954,7 +2022,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 27,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -1967,7 +2035,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"execution_count": 28,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -1986,12 +2054,12 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Bootstrap with Random Forests Instead of a Single Tree"
|
||||
"## Bootstrap with Random Forests Instead of a Single Tree, own Bagging"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -2007,7 +2075,7 @@
|
||||
"\n",
|
||||
"np.random.seed(2018)\n",
|
||||
"\n",
|
||||
"n = 40\n",
|
||||
"n = 100\n",
|
||||
"n_boostraps = 100\n",
|
||||
"maxdegree = 14\n",
|
||||
"\n",
|
||||
@@ -2031,7 +2099,7 @@
|
||||
" y_pred = np.empty((y_test.shape[0], n_boostraps))\n",
|
||||
" for i in range(n_boostraps):\n",
|
||||
" x_, y_ = resample(X_train_scaled, y_train)\n",
|
||||
" model.fit(x_, y_)\n",
|
||||
" model.fit(x_, y_.ravel())\n",
|
||||
" y_pred[:, i] = model.predict(X_test_scaled).ravel()\n",
|
||||
"\n",
|
||||
" polydegree[degree] = degree\n",
|
||||
@@ -2120,7 +2188,7 @@
|
||||
"source": [
|
||||
"1. Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.\n",
|
||||
"\n",
|
||||
"a. Fit thus a given classifier to the training using the weights $w_i$.\n",
|
||||
"a. Fit then a given classifier to the training using the weights $w_i$.\n",
|
||||
"\n",
|
||||
"b. Compute then $\\mathrm{err}$ and figure out which events are classified properly and which are classified wrongly.\n",
|
||||
"\n",
|
||||
@@ -2139,7 +2207,6 @@
|
||||
"new classification step $m$ is then forced to concentrate on those\n",
|
||||
"observations that are missed in the previous iterations.\n",
|
||||
"\n",
|
||||
"## Figure to Illustrate the Iterative Classification Process\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## AdaBoost Examples\n",
|
||||
@@ -2149,7 +2216,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -2239,7 +2306,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -2338,7 +2405,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"execution_count": 32,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -2423,12 +2490,75 @@
|
||||
"\n",
|
||||
"It is now the algorithm which wins essentially all ML competitions!!!\n",
|
||||
"\n",
|
||||
"## Regression Case"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"import xgboost as xgb\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"import scikitplot as skplt\n",
|
||||
"from sklearn.metrics import mean_squared_error\n",
|
||||
"\n",
|
||||
"n = 40\n",
|
||||
"n_boostraps = 100\n",
|
||||
"maxdegree = 8\n",
|
||||
"\n",
|
||||
"# Make data set.\n",
|
||||
"x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
|
||||
"y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n",
|
||||
"\n",
|
||||
"error = np.zeros(maxdegree)\n",
|
||||
"bias = np.zeros(maxdegree)\n",
|
||||
"variance = np.zeros(maxdegree)\n",
|
||||
"polydegree = np.zeros(maxdegree)\n",
|
||||
"X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
|
||||
"scaler = StandardScaler()\n",
|
||||
"scaler.fit(X_train)\n",
|
||||
"X_train_scaled = scaler.transform(X_train)\n",
|
||||
"X_test_scaled = scaler.transform(X_test)\n",
|
||||
"\n",
|
||||
"for degree in range(maxdegree):\n",
|
||||
" model = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,\n",
|
||||
" max_depth = maxdegree, alpha = 10, n_estimators = 10)\n",
|
||||
" model.fit(X_train_scaled,y_train)\n",
|
||||
" y_pred = model.predict(X_test_scaled)\n",
|
||||
" polydegree[degree] = degree\n",
|
||||
" error[degree] = np.mean( np.mean((y_test - y_pred)**2) )\n",
|
||||
" bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )\n",
|
||||
" variance[degree] = np.mean( np.var(y_pred) )\n",
|
||||
" print('Max depth:', degree)\n",
|
||||
" print('Error:', error[degree])\n",
|
||||
" print('Bias^2:', bias[degree])\n",
|
||||
" print('Var:', variance[degree])\n",
|
||||
" print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n",
|
||||
"\n",
|
||||
"plt.plot(polydegree, error, label='Error')\n",
|
||||
"plt.plot(polydegree, bias, label='bias')\n",
|
||||
"plt.plot(polydegree, variance, label='Variance')\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Xgboost on the Cancer Data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
|
||||
Binary file not shown.
Binary file not shown.
@@ -1449,6 +1449,69 @@ plt.show()
|
||||
!ec
|
||||
|
||||
|
||||
!split
|
||||
===== Changing the Level of the Decision Tree =====
|
||||
|
||||
!bc pycod
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.pipeline import make_pipeline
|
||||
from sklearn.utils import resample
|
||||
from sklearn.tree import DecisionTreeRegressor
|
||||
|
||||
n = 100
|
||||
n_boostraps = 100
|
||||
maxdepth = 8
|
||||
|
||||
# Make data set.
|
||||
x = np.linspace(-3, 3, n).reshape(-1, 1)
|
||||
y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
|
||||
error = np.zeros(maxdepth)
|
||||
bias = np.zeros(maxdepth)
|
||||
variance = np.zeros(maxdepth)
|
||||
polydegree = np.zeros(maxdepth)
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
|
||||
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
|
||||
for degree in range(1,maxdepth):
|
||||
model = DecisionTreeRegressor(max_depth=degree)
|
||||
y_pred = np.empty((y_test.shape[0], n_boostraps))
|
||||
for i in range(n_boostraps):
|
||||
x_, y_ = resample(X_train_scaled, y_train)
|
||||
model.fit(x_, y_)
|
||||
y_pred[:, i] = model.predict(X_test_scaled)#.ravel()
|
||||
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
|
||||
bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
|
||||
variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
|
||||
print('Polynomial degree:', degree)
|
||||
print('Error:', error[degree])
|
||||
print('Bias^2:', bias[degree])
|
||||
print('Var:', variance[degree])
|
||||
print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
|
||||
|
||||
plt.xlim(1,maxdepth)
|
||||
plt.plot(polydegree, error, label='Error')
|
||||
plt.plot(polydegree, bias, label='bias')
|
||||
plt.plot(polydegree, variance, label='Variance')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
!ec
|
||||
|
||||
|
||||
|
||||
|
||||
!split
|
||||
===== Random forests =====
|
||||
@@ -1599,7 +1662,7 @@ np.sum(y_pred == y_pred_rf) / len(y_pred)
|
||||
|
||||
|
||||
!split
|
||||
===== Bootstrap with Random Forests Instead of a Single Tree =====
|
||||
===== Bootstrap with Random Forests Instead of a Single Tree, own Bagging =====
|
||||
|
||||
!bc pycod
|
||||
|
||||
@@ -1612,7 +1675,7 @@ from sklearn.ensemble import RandomForestRegressor
|
||||
|
||||
np.random.seed(2018)
|
||||
|
||||
n = 40
|
||||
n = 100
|
||||
n_boostraps = 100
|
||||
maxdegree = 14
|
||||
|
||||
@@ -1636,7 +1699,7 @@ for degree in range(maxdegree):
|
||||
y_pred = np.empty((y_test.shape[0], n_boostraps))
|
||||
for i in range(n_boostraps):
|
||||
x_, y_ = resample(X_train_scaled, y_train)
|
||||
model.fit(x_, y_)
|
||||
model.fit(x_, y_.ravel())
|
||||
y_pred[:, i] = model.predict(X_test_scaled).ravel()
|
||||
|
||||
polydegree[degree] = degree
|
||||
@@ -1707,7 +1770,7 @@ o We rewrite the misclassification error as
|
||||
\]
|
||||
!et
|
||||
o Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.
|
||||
o Fit thus a given classifier to the training using the weights $w_i$.
|
||||
o Fit then a given classifier to the training using the weights $w_i$.
|
||||
o Compute then $\mathrm{err}$ and figure out which events are classified properly and which are classified wrongly.
|
||||
o Define a quantity $\alpha_{m} = \log{(1-\mathrm{err})/\mathrm{err}}
|
||||
o Set the new weights to $w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(\bm{X}_{i*})}.
|
||||
@@ -1721,8 +1784,6 @@ difficult to classifiy correctly are given a larger influence. Each
|
||||
new classification step $m$ is then forced to concentrate on those
|
||||
observations that are missed in the previous iterations.
|
||||
|
||||
!split
|
||||
===== Figure to Illustrate the Iterative Classification Process =====
|
||||
|
||||
|
||||
!split
|
||||
@@ -1965,6 +2026,62 @@ sketch for efficient proposal calculation. It introduces a novel sparsity-aware
|
||||
|
||||
It is now the algorithm which wins essentially all ML competitions!!!
|
||||
|
||||
!split
|
||||
===== Regression Case =====
|
||||
|
||||
!bc pycod
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from sklearn.model_selection import train_test_split
|
||||
import xgboost as xgb
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
import scikitplot as skplt
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
n = 40
|
||||
n_boostraps = 100
|
||||
maxdegree = 8
|
||||
|
||||
# Make data set.
|
||||
x = np.linspace(-3, 3, n).reshape(-1, 1)
|
||||
y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
|
||||
|
||||
error = np.zeros(maxdegree)
|
||||
bias = np.zeros(maxdegree)
|
||||
variance = np.zeros(maxdegree)
|
||||
polydegree = np.zeros(maxdegree)
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
|
||||
for degree in range(maxdegree):
|
||||
model = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
|
||||
max_depth = maxdegree, alpha = 10, n_estimators = 10)
|
||||
model.fit(X_train_scaled,y_train)
|
||||
y_pred = model.predict(X_test_scaled)
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
|
||||
bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
|
||||
variance[degree] = np.mean( np.var(y_pred) )
|
||||
print('Max depth:', degree)
|
||||
print('Error:', error[degree])
|
||||
print('Bias^2:', bias[degree])
|
||||
print('Var:', variance[degree])
|
||||
print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
|
||||
|
||||
plt.plot(polydegree, error, label='Error')
|
||||
plt.plot(polydegree, bias, label='bias')
|
||||
plt.plot(polydegree, variance, label='Variance')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
|
||||
!ec
|
||||
|
||||
|
||||
|
||||
!split
|
||||
===== Xgboost on the Cancer Data =====
|
||||
!bc pycod
|
||||
|
||||
@@ -0,0 +1,56 @@
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.pipeline import make_pipeline
|
||||
from sklearn.utils import resample
|
||||
from sklearn.tree import DecisionTreeRegressor
|
||||
|
||||
|
||||
np.random.seed(2018)
|
||||
|
||||
n = 40
|
||||
n_boostraps = 100
|
||||
maxdegree = 14
|
||||
|
||||
# Make data set.
|
||||
x = np.linspace(-3, 3, n).reshape(-1, 1)
|
||||
y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
|
||||
error = np.zeros(maxdegree)
|
||||
bias = np.zeros(maxdegree)
|
||||
variance = np.zeros(maxdegree)
|
||||
polydegree = np.zeros(maxdegree)
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
|
||||
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
|
||||
for degree in range(maxdegree):
|
||||
model = DecisionTreeRegressor(max_depth=2)
|
||||
y_pred = np.empty((y_test.shape[0], n_boostraps))
|
||||
for i in range(n_boostraps):
|
||||
x_, y_ = resample(X_train_scaled, y_train)
|
||||
model.fit(x_, y_)
|
||||
y_pred[:, i] = model.predict(X_test_scaled).ravel()
|
||||
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
|
||||
bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
|
||||
variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
|
||||
print('Polynomial degree:', degree)
|
||||
print('Error:', error[degree])
|
||||
print('Bias^2:', bias[degree])
|
||||
print('Var:', variance[degree])
|
||||
print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
|
||||
|
||||
plt.plot(polydegree, error, label='Error')
|
||||
plt.plot(polydegree, bias, label='bias')
|
||||
plt.plot(polydegree, variance, label='Variance')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,54 @@
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.pipeline import make_pipeline
|
||||
from sklearn.utils import resample
|
||||
from sklearn.tree import DecisionTreeRegressor
|
||||
|
||||
n = 100
|
||||
n_boostraps = 100
|
||||
maxdepth = 8
|
||||
|
||||
# Make data set.
|
||||
x = np.linspace(-3, 3, n).reshape(-1, 1)
|
||||
y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
|
||||
error = np.zeros(maxdepth)
|
||||
bias = np.zeros(maxdepth)
|
||||
variance = np.zeros(maxdepth)
|
||||
polydegree = np.zeros(maxdepth)
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
|
||||
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
|
||||
for degree in range(1,maxdepth):
|
||||
model = DecisionTreeRegressor(max_depth=degree)
|
||||
y_pred = np.empty((y_test.shape[0], n_boostraps))
|
||||
for i in range(n_boostraps):
|
||||
x_, y_ = resample(X_train_scaled, y_train)
|
||||
model.fit(x_, y_)
|
||||
y_pred[:, i] = model.predict(X_test_scaled)#.ravel()
|
||||
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
|
||||
bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
|
||||
variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
|
||||
print('Polynomial degree:', degree)
|
||||
print('Error:', error[degree])
|
||||
print('Bias^2:', bias[degree])
|
||||
print('Var:', variance[degree])
|
||||
print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
|
||||
|
||||
plt.xlim(1,maxdepth)
|
||||
plt.plot(polydegree, error, label='Error')
|
||||
plt.plot(polydegree, bias, label='bias')
|
||||
plt.plot(polydegree, variance, label='Variance')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,54 @@
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.pipeline import make_pipeline
|
||||
from sklearn.utils import resample
|
||||
from sklearn.tree import DecisionTreeRegressor
|
||||
|
||||
n = 100
|
||||
n_boostraps = 100
|
||||
maxdepth = 8
|
||||
|
||||
# Make data set.
|
||||
x = np.linspace(-3, 3, n).reshape(-1, 1)
|
||||
y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
|
||||
error = np.zeros(maxdepth)
|
||||
bias = np.zeros(maxdepth)
|
||||
variance = np.zeros(maxdepth)
|
||||
polydegree = np.zeros(maxdepth)
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
|
||||
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
|
||||
for degree in range(1,maxdepth):
|
||||
model = DecisionTreeRegressor(max_depth=degree)
|
||||
y_pred = np.empty((y_test.shape[0], n_boostraps))
|
||||
for i in range(n_boostraps):
|
||||
x_, y_ = resample(X_train_scaled, y_train)
|
||||
model.fit(x_, y_)
|
||||
y_pred[:, i] = model.predict(X_test_scaled)#.ravel()
|
||||
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
|
||||
bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
|
||||
variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
|
||||
print('Polynomial degree:', degree)
|
||||
print('Error:', error[degree])
|
||||
print('Bias^2:', bias[degree])
|
||||
print('Var:', variance[degree])
|
||||
print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
|
||||
|
||||
plt.xlim(1,maxdegree)
|
||||
plt.plot(polydegree, error, label='Error')
|
||||
plt.plot(polydegree, bias, label='bias')
|
||||
plt.plot(polydegree, variance, label='Variance')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
@@ -8,13 +8,14 @@ from sklearn.ensemble import RandomForestRegressor
|
||||
|
||||
np.random.seed(2018)
|
||||
|
||||
n = 40
|
||||
n = 500
|
||||
n_boostraps = 100
|
||||
maxdegree = 14
|
||||
|
||||
# Make data set.
|
||||
x = np.linspace(-3, 3, n).reshape(-1, 1)
|
||||
y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
|
||||
|
||||
error = np.zeros(maxdegree)
|
||||
bias = np.zeros(maxdegree)
|
||||
variance = np.zeros(maxdegree)
|
||||
@@ -32,8 +33,8 @@ for degree in range(maxdegree):
|
||||
y_pred = np.empty((y_test.shape[0], n_boostraps))
|
||||
for i in range(n_boostraps):
|
||||
x_, y_ = resample(X_train_scaled, y_train)
|
||||
model.fit(x_, y_)
|
||||
y_pred[:, i] = model.predict(X_test_scaled)#.ravel()
|
||||
model.fit(x_, y_.ravel())
|
||||
y_pred[:, i] = model.predict(X_test_scaled)
|
||||
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
|
||||
|
||||
@@ -0,0 +1,54 @@
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.pipeline import make_pipeline
|
||||
from sklearn.utils import resample
|
||||
from sklearn.ensemble import RandomForestRegressor
|
||||
|
||||
np.random.seed(2018)
|
||||
|
||||
n = 40
|
||||
n_boostraps = 100
|
||||
maxdegree = 14
|
||||
|
||||
# Make data set.
|
||||
x = np.linspace(-3, 3, n).reshape(-1, 1)
|
||||
y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
|
||||
error = np.zeros(maxdegree)
|
||||
bias = np.zeros(maxdegree)
|
||||
variance = np.zeros(maxdegree)
|
||||
polydegree = np.zeros(maxdegree)
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
|
||||
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
|
||||
for degree in range(maxdegree):
|
||||
model = RandomForestRegressor()
|
||||
y_pred = np.empty((y_test.shape[0], n_boostraps))
|
||||
for i in range(n_boostraps):
|
||||
x_, y_ = resample(X_train_scaled, y_train).ravel()
|
||||
model.fit(x_, y_)
|
||||
y_pred[:, i] = model.predict(X_test_scaled).ravel()
|
||||
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
|
||||
bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
|
||||
variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
|
||||
print('Polynomial degree:', degree)
|
||||
print('Error:', error[degree])
|
||||
print('Bias^2:', bias[degree])
|
||||
print('Var:', variance[degree])
|
||||
print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
|
||||
|
||||
plt.plot(polydegree, error, label='Error')
|
||||
plt.plot(polydegree, bias, label='bias')
|
||||
plt.plot(polydegree, variance, label='Variance')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
|
||||
@@ -29,8 +29,6 @@ accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_
|
||||
print(accuracy)
|
||||
print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(Random_Forest_model.score(X_test_scaled,y_test)))
|
||||
|
||||
|
||||
|
||||
y_pred = Random_Forest_model.predict(X_test_scaled)
|
||||
skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
|
||||
plt.show()
|
||||
|
||||
@@ -0,0 +1,41 @@
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.datasets import load_breast_cancer
|
||||
from sklearn.tree import DecisionTreeClassifier
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
from sklearn.model_selection import cross_validate
|
||||
import scikitplot as skplt
|
||||
|
||||
# Load the data
|
||||
cancer = load_breast_cancer()
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
|
||||
print(X_train.shape)
|
||||
print(X_test.shape)
|
||||
#now scale the data
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
# Data set not specificied
|
||||
#Instantiate the model with 100 trees and entropy as splitting criteria
|
||||
Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion="entropy")
|
||||
Random_Forest_model.fit(X_train_scaled, y_train)
|
||||
#Cross validation
|
||||
accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']
|
||||
print(accuracy)
|
||||
print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(Random_Forest_model.score(X_test_scaled,y_test)))
|
||||
|
||||
|
||||
|
||||
y_pred = Random_Forest_model.predict(X_test_scaled)
|
||||
skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
|
||||
plt.show()
|
||||
y_probas = Random_Forest_model.predict_proba(X_test_scaled)
|
||||
skplt.metrics.plot_roc(y_test, y_probas)
|
||||
plt.show()
|
||||
skplt.metrics.plot_cumulative_gain(y_test, y_probas)
|
||||
plt.show()
|
||||
@@ -0,0 +1,47 @@
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from sklearn.model_selection import train_test_split
|
||||
import xgboost as xgb
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
import scikitplot as skplt
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
n = 40
|
||||
n_boostraps = 100
|
||||
maxdegree = 8
|
||||
|
||||
# Make data set.
|
||||
x = np.linspace(-3, 3, n).reshape(-1, 1)
|
||||
y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
|
||||
|
||||
error = np.zeros(maxdegree)
|
||||
bias = np.zeros(maxdegree)
|
||||
variance = np.zeros(maxdegree)
|
||||
polydegree = np.zeros(maxdegree)
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
|
||||
for degree in range(maxdegree):
|
||||
model = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
|
||||
max_depth = maxdegree, alpha = 10, n_estimators = 10)
|
||||
model.fit(X_train_scaled,y_train)
|
||||
y_pred = model.predict(X_test_scaled)
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
|
||||
bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
|
||||
variance[degree] = np.mean( np.var(y_pred) )
|
||||
print('Polynomial degree:', degree)
|
||||
print('Error:', error[degree])
|
||||
print('Bias^2:', bias[degree])
|
||||
print('Var:', variance[degree])
|
||||
print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
|
||||
|
||||
plt.plot(polydegree, error, label='Error')
|
||||
plt.plot(polydegree, bias, label='bias')
|
||||
plt.plot(polydegree, variance, label='Variance')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
@@ -0,0 +1,47 @@
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from sklearn.model_selection import train_test_split
|
||||
import xgboost as xgb
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
import scikitplot as skplt
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
n = 500
|
||||
n_boostraps = 100
|
||||
maxdegree = 8
|
||||
|
||||
# Make data set.
|
||||
x = np.linspace(-3, 3, n).reshape(-1, 1)
|
||||
y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
|
||||
|
||||
error = np.zeros(maxdegree)
|
||||
bias = np.zeros(maxdegree)
|
||||
variance = np.zeros(maxdegree)
|
||||
polydegree = np.zeros(maxdegree)
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(X_train)
|
||||
X_train_scaled = scaler.transform(X_train)
|
||||
X_test_scaled = scaler.transform(X_test)
|
||||
|
||||
for degree in range(maxdegree):
|
||||
model = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
|
||||
max_depth = maxdegree, alpha = 10, n_estimators = 10)
|
||||
model.fit(X_train_scaled,y_train)
|
||||
y_pred = model.predict(X_test_scaled)
|
||||
polydegree[degree] = degree
|
||||
error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
|
||||
bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
|
||||
variance[degree] = np.mean( np.var(y_pred) )
|
||||
print('Polynomial degree:', degree)
|
||||
print('Error:', error[degree])
|
||||
print('Bias^2:', bias[degree])
|
||||
print('Var:', variance[degree])
|
||||
print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
|
||||
|
||||
plt.plot(polydegree, error, label='Error')
|
||||
plt.plot(polydegree, bias, label='bias')
|
||||
plt.plot(polydegree, variance, label='Variance')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
Reference in New Issue
Block a user