added steepest descent boosting
This commit is contained in:
mhjensen
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
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'___sec51'),
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('Basic Steps of AdaBoost', 2, None, '___sec52'),
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('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
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2,
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None,
|
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'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
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2,
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None,
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||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
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('Gradient Boosting, Examples of Regression',
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||||
2,
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||||
None,
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||||
'___sec56'),
|
||||
'___sec57'),
|
||||
('Gradient Boosting, Classification Example',
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||||
2,
|
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None,
|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
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('Regression Case', 2, None, '___sec59'),
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('Xgboost on the Cancer Data', 2, None, '___sec60')]}
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'___sec58'),
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('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
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('Regression Case', 2, None, '___sec60'),
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('Xgboost on the Cancer Data', 2, None, '___sec61')]}
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end of tocinfo -->
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<body>
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@@ -244,13 +251,14 @@ MathJax.Hub.Config({
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
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</li>
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@@ -309,7 +317,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</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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||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
('Gradient Boosting, Examples of Regression',
|
||||
2,
|
||||
None,
|
||||
'___sec56'),
|
||||
'___sec57'),
|
||||
('Gradient Boosting, Classification Example',
|
||||
2,
|
||||
None,
|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
('Regression Case', 2, None, '___sec59'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec60')]}
|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
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||||
</li>
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@@ -314,7 +322,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs002.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
('Gradient Boosting, Examples of Regression',
|
||||
2,
|
||||
None,
|
||||
'___sec56'),
|
||||
'___sec57'),
|
||||
('Gradient Boosting, Classification Example',
|
||||
2,
|
||||
None,
|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
('Regression Case', 2, None, '___sec59'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec60')]}
|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -292,7 +300,7 @@ This tree was produced using the Wisconsin cancer data (discussed here as well,
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<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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs003.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
('Gradient Boosting, Examples of Regression',
|
||||
2,
|
||||
None,
|
||||
'___sec56'),
|
||||
'___sec57'),
|
||||
('Gradient Boosting, Classification Example',
|
||||
2,
|
||||
None,
|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
('Regression Case', 2, None, '___sec59'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec60')]}
|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -300,7 +308,7 @@ node.
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<li><a href="._DecisionTrees-bs011.html">12</a></li>
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||||
<li><a href="._DecisionTrees-bs012.html">13</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs004.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
('Gradient Boosting, Examples of Regression',
|
||||
2,
|
||||
None,
|
||||
'___sec56'),
|
||||
'___sec57'),
|
||||
('Gradient Boosting, Classification Example',
|
||||
2,
|
||||
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|
||||
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|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
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|
||||
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|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
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|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -301,7 +309,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs005.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
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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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|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
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|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -380,7 +388,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs006.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
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|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('The Squared-Error again! Steepest Descent',
|
||||
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|
||||
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|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
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|
||||
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|
||||
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|
||||
'___sec56'),
|
||||
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|
||||
('Gradient Boosting, Classification Example',
|
||||
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|
||||
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|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
('Regression Case', 2, None, '___sec59'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec60')]}
|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -313,7 +321,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs007.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Gradient Boosting, Classification Example',
|
||||
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|
||||
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|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
('Regression Case', 2, None, '___sec59'),
|
||||
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|
||||
'___sec58'),
|
||||
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|
||||
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|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
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|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -305,7 +313,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs008.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
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|
||||
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|
||||
'___sec55'),
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||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
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|
||||
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||||
'___sec56'),
|
||||
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('Gradient Boosting, Classification Example',
|
||||
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|
||||
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|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
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|
||||
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|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
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end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -338,7 +346,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs009.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -307,7 +315,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs010.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -320,7 +328,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs011.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -316,7 +324,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs012.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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|
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<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -308,7 +316,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs013.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
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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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||||
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|
||||
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||||
|
||||
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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -313,7 +321,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs014.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -339,7 +347,7 @@ $$
|
||||
<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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs015.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
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|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
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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>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -330,7 +338,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs016.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
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('Gradient Boosting, algorithm', 2, None, '___sec55'),
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|
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||||
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|
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('The Squared-Error again! Steepest Descent',
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|
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|
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'___sec58'),
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|
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|
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<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -321,7 +329,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs017.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -304,7 +312,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs018.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
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|
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|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -294,7 +302,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs019.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -294,7 +302,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs020.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
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|
||||
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|
||||
'___sec55'),
|
||||
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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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'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
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|
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|
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|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -334,7 +342,7 @@ 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>
|
||||
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|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
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||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
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||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
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|
||||
@@ -365,7 +373,7 @@ 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>
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||||
<li><a href="">...</a></li>
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<li><a href="._DecisionTrees-bs061.html">62</a></li>
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||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
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<li><a href="._DecisionTrees-bs022.html">»</a></li>
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||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
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||||
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|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -367,7 +375,7 @@ 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>
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||||
<li><a href="">...</a></li>
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<li><a href="._DecisionTrees-bs061.html">62</a></li>
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<li><a href="._DecisionTrees-bs062.html">63</a></li>
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<li><a href="._DecisionTrees-bs023.html">»</a></li>
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||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
||||
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|
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -325,7 +333,7 @@ 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>
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||||
<li><a href="">...</a></li>
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||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
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||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
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<li><a href="._DecisionTrees-bs024.html">»</a></li>
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</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
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|
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|
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@@ -485,7 +493,7 @@ MathJax.Hub.Config({
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||||
<li><a href="._DecisionTrees-bs032.html">33</a></li>
|
||||
<li><a href="._DecisionTrees-bs033.html">34</a></li>
|
||||
<li><a href="">...</a></li>
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||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
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<li><a href="._DecisionTrees-bs062.html">63</a></li>
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<li><a href="._DecisionTrees-bs025.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -338,7 +346,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs026.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
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|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
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|
||||
@@ -361,7 +369,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs027.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
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|
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -317,7 +325,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs028.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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|
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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -311,7 +319,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs029.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
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||||
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|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
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|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -367,7 +375,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs030.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -303,7 +311,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs031.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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|
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|
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|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -308,7 +316,7 @@ trees can be substantially improved.
|
||||
<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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs032.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -316,7 +324,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs033.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
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|
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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -297,7 +305,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs034.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -308,7 +316,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs035.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -318,7 +326,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs036.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -310,7 +318,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs037.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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|
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<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -340,7 +348,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs038.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
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@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -348,7 +356,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs039.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
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|
||||
@@ -351,7 +359,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs040.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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|
||||
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|
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -352,7 +360,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs041.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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|
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@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -333,7 +341,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs042.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
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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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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -315,7 +323,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs043.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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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'___sec58'),
|
||||
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|
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|
||||
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|
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|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -362,7 +370,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs044.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
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|
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|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
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|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
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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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'___sec58'),
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||||
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||||
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|
||||
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|
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|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -311,7 +319,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs045.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
2,
|
||||
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|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
('Gradient Boosting, Examples of Regression',
|
||||
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|
||||
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|
||||
'___sec56'),
|
||||
'___sec57'),
|
||||
('Gradient Boosting, Classification Example',
|
||||
2,
|
||||
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|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
('Regression Case', 2, None, '___sec59'),
|
||||
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|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
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|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -306,7 +314,7 @@ them with a factor.
|
||||
<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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
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|
||||
</ul>
|
||||
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|
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -341,7 +349,7 @@ In iterative fitting or additive modeling, we minimize the cost function with re
|
||||
<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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs047.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
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|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
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|
||||
@@ -314,7 +322,7 @@ at the internal nodes, and the predictions at the terminal nodes.
|
||||
<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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs048.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
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|
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -337,7 +345,7 @@ The solution to these two equations gives us in turn \( \beta_1 \) and \( \gamma
|
||||
<li><a href="._DecisionTrees-bs056.html">57</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
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||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs049.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -324,7 +332,7 @@ $$
|
||||
<li><a href="._DecisionTrees-bs057.html">58</a></li>
|
||||
<li><a href="._DecisionTrees-bs058.html">59</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs050.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -317,7 +325,7 @@ where we have defined \( w_i^m= \exp{(-y_if_{m-1}(x_i))} \).
|
||||
<li><a href="._DecisionTrees-bs058.html">59</a></li>
|
||||
<li><a href="._DecisionTrees-bs059.html">60</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs051.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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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|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -332,7 +340,7 @@ $$
|
||||
<li><a href="._DecisionTrees-bs059.html">60</a></li>
|
||||
<li><a href="._DecisionTrees-bs060.html">61</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs052.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -310,6 +318,8 @@ where the function \( I() \) is one if we misclassify and zero if we classify co
|
||||
<li><a href="._DecisionTrees-bs059.html">60</a></li>
|
||||
<li><a href="._DecisionTrees-bs060.html">61</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs053.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -327,6 +335,7 @@ observations that are missed in the previous iterations.
|
||||
<li><a href="._DecisionTrees-bs059.html">60</a></li>
|
||||
<li><a href="._DecisionTrees-bs060.html">61</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs054.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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|
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|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -319,6 +327,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs059.html">60</a></li>
|
||||
<li><a href="._DecisionTrees-bs060.html">61</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs055.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
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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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|
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|
||||
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|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
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|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -266,7 +274,7 @@ MathJax.Hub.Config({
|
||||
<a name="part0055"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec54" class="anchor">Gradient boosting: Basics </h2>
|
||||
<h2 id="___sec54" class="anchor">Gradient boosting: Basics with Steepest Descent </h2>
|
||||
|
||||
<p>
|
||||
Gradient boosting is again a similar technique to Adaptive boosting,
|
||||
@@ -278,9 +286,6 @@ In order to understand the method, let us illustrate its basics by
|
||||
bringing back the essential steps in linear regression, where our cost
|
||||
function was the least squares function.
|
||||
|
||||
<p>
|
||||
See discussion during lecture November 8.
|
||||
|
||||
<p>
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
@@ -303,6 +308,7 @@ See discussion during lecture November 8.
|
||||
<li><a href="._DecisionTrees-bs059.html">60</a></li>
|
||||
<li><a href="._DecisionTrees-bs060.html">61</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs056.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
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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, '___sec61')]}
|
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|
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|
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<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -266,30 +274,17 @@ MathJax.Hub.Config({
|
||||
<a name="part0056"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec55" class="anchor">Gradient Boosting, algorithm </h2>
|
||||
<h2 id="___sec55" class="anchor">The Squared-Error again! Steepest Descent </h2>
|
||||
|
||||
<p>
|
||||
Suppose we have a cost function \( C(f)=\sum_{i=0}^{n-1}L(y_i, f(x_i)) \) where \( y_i \) is our target and \( f(x_i) \) the function which is meant to model \( y_i \). The above cost function could be our standard squared-error function
|
||||
We start again with our cost function \( {\cal C}(\boldsymbol{y}m\boldsymbol{f})=\sum_{i=0}^{n-1}{\cal L}(y_i, f(x_i)) \) where we want to minimize
|
||||
This means that for every iteration, we need to optimize
|
||||
|
||||
$$
|
||||
C(\boldsymbol{y},\boldsymbol{f})=\sum_{i=0}^{n-1}(y_i-f(x_i))^2.
|
||||
(\hat{\boldsymbol{f}}) \mathrm{argmin}_{\boldsymbol{f}}\hspace{0.1cm} \sum_{i=0}^{n-1}(y_i-f(x_i))^2.
|
||||
$$
|
||||
|
||||
<p>
|
||||
The way we proceed in an iterative fashion is to
|
||||
|
||||
<ol>
|
||||
<li> Initialize our estimate \( f_0(x) \).</li>
|
||||
<li> For \( m=1:M \), we
|
||||
|
||||
<ol type="a"></li>
|
||||
<li> compute the negative gradient vector \( \boldsymbol{u}_m = -\partial C(\boldsymbol{y},\boldsymbol{f})/\partial \boldsymbol{f}(x) \) at \( f(x) = f_{m-1}(x) \);</li>
|
||||
<li> fit the so-called base-learner to the negative gradient \( h_m(u_m,x) \);</li>
|
||||
<li> update the estimate \( f_m(x) = f_{m-1}(x)+\nu h_m(u_m,x) \);</li>
|
||||
</ol>
|
||||
|
||||
<li> The final estimate is then \( f_M(x) = \sum_{m=1}^M\nu h_m(u_m,x) \).</li>
|
||||
</ol>
|
||||
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
<ul class="pagination">
|
||||
@@ -310,6 +305,7 @@ The way we proceed in an iterative fashion is to
|
||||
<li><a href="._DecisionTrees-bs059.html">60</a></li>
|
||||
<li><a href="._DecisionTrees-bs060.html">61</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs057.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
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|
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|
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|
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('Gradient boosting: Basics with Steepest Descent',
|
||||
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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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'___sec58'),
|
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|
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('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
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end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -266,58 +274,30 @@ MathJax.Hub.Config({
|
||||
<a name="part0057"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec56" class="anchor">Gradient Boosting, Examples of Regression </h2>
|
||||
<h2 id="___sec56" class="anchor">Gradient Boosting, algorithm </h2>
|
||||
|
||||
<p>
|
||||
Suppose we have a cost function \( C(f)=\sum_{i=0}^{n-1}L(y_i, f(x_i)) \) where \( y_i \) is our target and \( f(x_i) \) the function which is meant to model \( y_i \). The above cost function could be our standard squared-error function
|
||||
$$
|
||||
C(\boldsymbol{y},\boldsymbol{f})=\sum_{i=0}^{n-1}(y_i-f(x_i))^2.
|
||||
$$
|
||||
|
||||
<!-- 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.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> GradientBoostingRegressor
|
||||
<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">100</span>
|
||||
maxdegree <span style="color: #666666">=</span> <span style="color: #666666">6</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>(<span style="color: #666666">1</span>,maxdegree):
|
||||
model <span style="color: #666666">=</span> GradientBoostingRegressor(max_depth<span style="color: #666666">=</span>degree, n_estimators<span style="color: #666666">=100</span>, learning_rate<span style="color: #666666">=1.0</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>xlim(<span style="color: #666666">1</span>,maxdegree<span style="color: #666666">-1</span>)
|
||||
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()
|
||||
save_fig(<span style="color: #BA2121">"gdregression"</span>)
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
</pre></div>
|
||||
<p>
|
||||
The way we proceed in an iterative fashion is to
|
||||
|
||||
<ol>
|
||||
<li> Initialize our estimate \( f_0(x) \).</li>
|
||||
<li> For \( m=1:M \), we
|
||||
|
||||
<ol type="a"></li>
|
||||
<li> compute the negative gradient vector \( \boldsymbol{u}_m = -\partial C(\boldsymbol{y},\boldsymbol{f})/\partial \boldsymbol{f}(x) \) at \( f(x) = f_{m-1}(x) \);</li>
|
||||
<li> fit the so-called base-learner to the negative gradient \( h_m(u_m,x) \);</li>
|
||||
<li> update the estimate \( f_m(x) = f_{m-1}(x)+\nu h_m(u_m,x) \);</li>
|
||||
</ol>
|
||||
|
||||
<li> The final estimate is then \( f_M(x) = \sum_{m=1}^M\nu h_m(u_m,x) \).</li>
|
||||
</ol>
|
||||
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
<ul class="pagination">
|
||||
@@ -337,6 +317,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs059.html">60</a></li>
|
||||
<li><a href="._DecisionTrees-bs060.html">61</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs058.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
('Gradient Boosting, Examples of Regression',
|
||||
2,
|
||||
None,
|
||||
'___sec56'),
|
||||
'___sec57'),
|
||||
('Gradient Boosting, Classification Example',
|
||||
2,
|
||||
None,
|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
('Regression Case', 2, None, '___sec59'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec60')]}
|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -266,49 +274,55 @@ MathJax.Hub.Config({
|
||||
<a name="part0058"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec57" class="anchor">Gradient Boosting, Classification Example </h2>
|
||||
<h2 id="___sec57" class="anchor">Gradient Boosting, Examples of Regression </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.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_breast_cancer
|
||||
<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.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> GradientBoostingClassifier
|
||||
<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"># 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">#now scale the data</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.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> GradientBoostingRegressor
|
||||
<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">100</span>
|
||||
maxdegree <span style="color: #666666">=</span> <span style="color: #666666">6</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)
|
||||
|
||||
gd_clf <span style="color: #666666">=</span> GradientBoostingClassifier(max_depth<span style="color: #666666">=3</span>, n_estimators<span style="color: #666666">=100</span>, learning_rate<span style="color: #666666">=1.0</span>)
|
||||
gd_clf<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(gd_clf,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(gd_clf<span style="color: #666666">.</span>score(X_test_scaled,y_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>,maxdegree):
|
||||
model <span style="color: #666666">=</span> GradientBoostingRegressor(max_depth<span style="color: #666666">=</span>degree, n_estimators<span style="color: #666666">=100</span>, learning_rate<span style="color: #666666">=1.0</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]))
|
||||
|
||||
<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> gd_clf<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>)
|
||||
save_fig(<span style="color: #BA2121">"gdclassiffierconfusion"</span>)
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
y_probas <span style="color: #666666">=</span> gd_clf<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)
|
||||
save_fig(<span style="color: #BA2121">"gdclassiffierroc"</span>)
|
||||
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)
|
||||
save_fig(<span style="color: #BA2121">"gdclassiffiercgain"</span>)
|
||||
plt<span style="color: #666666">.</span>xlim(<span style="color: #666666">1</span>,maxdegree<span style="color: #666666">-1</span>)
|
||||
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()
|
||||
save_fig(<span style="color: #BA2121">"gdregression"</span>)
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
</pre></div>
|
||||
<p>
|
||||
@@ -330,6 +344,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs059.html">60</a></li>
|
||||
<li><a href="._DecisionTrees-bs060.html">61</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs059.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
('Gradient Boosting, Examples of Regression',
|
||||
2,
|
||||
None,
|
||||
'___sec56'),
|
||||
'___sec57'),
|
||||
('Gradient Boosting, Classification Example',
|
||||
2,
|
||||
None,
|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
('Regression Case', 2, None, '___sec59'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec60')]}
|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -266,24 +274,51 @@ MathJax.Hub.Config({
|
||||
<a name="part0059"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec58" class="anchor">XGBoost: Extreme Gradient Boosting </h2>
|
||||
|
||||
<h2 id="___sec58" class="anchor">Gradient Boosting, Classification Example </h2>
|
||||
<p>
|
||||
<a href="https://github.com/dmlc/xgboost" target="_self">XGBoost</a> or Extreme Gradient
|
||||
Boosting, is an optimized distributed gradient boosting library
|
||||
designed to be highly efficient, flexible and portable. It implements
|
||||
machine learning algorithms under the Gradient Boosting
|
||||
framework. XGBoost provides a parallel tree boosting that solve many
|
||||
data science problems in a fast and accurate way. See the <a href="https://arxiv.org/abs/1603.02754" target="_self">article by Chen and Guestrin</a>.
|
||||
|
||||
<p>
|
||||
The authors design and build a highly scalable end-to-end tree
|
||||
boosting system. It has a theoretically justified weighted quantile
|
||||
sketch for efficient proposal calculation. It introduces a novel sparsity-aware algorithm for parallel tree learning and an effective cache-aware block structure for out-of-core tree learning.
|
||||
<!-- 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">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.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> GradientBoostingClassifier
|
||||
<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
|
||||
|
||||
<p>
|
||||
It is now the algorithm which wins essentially all ML competitions!!!
|
||||
<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">#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)
|
||||
|
||||
gd_clf <span style="color: #666666">=</span> GradientBoostingClassifier(max_depth<span style="color: #666666">=3</span>, n_estimators<span style="color: #666666">=100</span>, learning_rate<span style="color: #666666">=1.0</span>)
|
||||
gd_clf<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(gd_clf,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(gd_clf<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> gd_clf<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>)
|
||||
save_fig(<span style="color: #BA2121">"gdclassiffierconfusion"</span>)
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
y_probas <span style="color: #666666">=</span> gd_clf<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)
|
||||
save_fig(<span style="color: #BA2121">"gdclassiffierroc"</span>)
|
||||
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)
|
||||
save_fig(<span style="color: #BA2121">"gdclassiffiercgain"</span>)
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
</pre></div>
|
||||
<p>
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
@@ -302,6 +337,7 @@ It is now the algorithm which wins essentially all ML competitions!!!
|
||||
<li class="active"><a href="._DecisionTrees-bs059.html">60</a></li>
|
||||
<li><a href="._DecisionTrees-bs060.html">61</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs060.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
('Gradient Boosting, Examples of Regression',
|
||||
2,
|
||||
None,
|
||||
'___sec56'),
|
||||
'___sec57'),
|
||||
('Gradient Boosting, Classification Example',
|
||||
2,
|
||||
None,
|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
('Regression Case', 2, None, '___sec59'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec60')]}
|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -266,58 +274,24 @@ MathJax.Hub.Config({
|
||||
<a name="part0060"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec59" class="anchor">Regression Case </h2>
|
||||
<h2 id="___sec59" class="anchor">XGBoost: Extreme Gradient Boosting </h2>
|
||||
|
||||
<p>
|
||||
<a href="https://github.com/dmlc/xgboost" target="_self">XGBoost</a> or Extreme Gradient
|
||||
Boosting, is an optimized distributed gradient boosting library
|
||||
designed to be highly efficient, flexible and portable. It implements
|
||||
machine learning algorithms under the Gradient Boosting
|
||||
framework. XGBoost provides a parallel tree boosting that solve many
|
||||
data science problems in a fast and accurate way. See the <a href="https://arxiv.org/abs/1603.02754" target="_self">article by Chen and Guestrin</a>.
|
||||
|
||||
<!-- 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
|
||||
<p>
|
||||
The authors design and build a highly scalable end-to-end tree
|
||||
boosting system. It has a theoretically justified weighted quantile
|
||||
sketch for efficient proposal calculation. It introduces a novel sparsity-aware algorithm for parallel tree learning and an effective cache-aware block structure for out-of-core tree learning.
|
||||
|
||||
n <span style="color: #666666">=</span> <span style="color: #666666">100</span>
|
||||
maxdegree <span style="color: #666666">=</span> <span style="color: #666666">6</span>
|
||||
<p>
|
||||
It is now the algorithm which wins essentially all ML competitions!!!
|
||||
|
||||
<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:squarederror'</span>, colsaobjective <span style="color: #666666">=</span><span style="color: #BA2121">'reg:squarederror'</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> degree, alpha <span style="color: #666666">=</span> <span style="color: #666666">10</span>, n_estimators <span style="color: #666666">=</span> <span style="color: #666666">200</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>xlim(<span style="color: #666666">1</span>,maxdegree<span style="color: #666666">-1</span>)
|
||||
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 -->
|
||||
@@ -335,6 +309,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<li><a href="._DecisionTrees-bs059.html">60</a></li>
|
||||
<li class="active"><a href="._DecisionTrees-bs060.html">61</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs061.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -140,19 +140,26 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
('Gradient Boosting, Examples of Regression',
|
||||
2,
|
||||
None,
|
||||
'___sec56'),
|
||||
'___sec57'),
|
||||
('Gradient Boosting, Classification Example',
|
||||
2,
|
||||
None,
|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
('Regression Case', 2, None, '___sec59'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec60')]}
|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -244,13 +251,14 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">AdaBoost Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs057.html#___sec56" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs058.html#___sec57" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs059.html#___sec58" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs060.html#___sec59" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs061.html#___sec60" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DecisionTrees-bs062.html#___sec61" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -309,7 +317,7 @@ 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-bs061.html">62</a></li>
|
||||
<li><a href="._DecisionTrees-bs062.html">63</a></li>
|
||||
<li><a href="._DecisionTrees-bs001.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -2355,7 +2355,7 @@ plt.show()
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec54">Gradient boosting: Basics </h2>
|
||||
<h2 id="___sec54">Gradient boosting: Basics with Steepest Descent </h2>
|
||||
|
||||
<p>
|
||||
Gradient boosting is again a similar technique to Adaptive boosting,
|
||||
@@ -2366,14 +2366,26 @@ method via a series of iterations.
|
||||
In order to understand the method, let us illustrate its basics by
|
||||
bringing back the essential steps in linear regression, where our cost
|
||||
function was the least squares function.
|
||||
|
||||
<p>
|
||||
See discussion during lecture November 8.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec55">Gradient Boosting, algorithm </h2>
|
||||
<h2 id="___sec55">The Squared-Error again! Steepest Descent </h2>
|
||||
|
||||
<p>
|
||||
We start again with our cost function \( {\cal C}(\boldsymbol{y}m\boldsymbol{f})=\sum_{i=0}^{n-1}{\cal L}(y_i, f(x_i)) \) where we want to minimize
|
||||
This means that for every iteration, we need to optimize
|
||||
|
||||
<p> <br>
|
||||
$$
|
||||
(\hat{\boldsymbol{f}}) \mathrm{argmin}_{\boldsymbol{f}}\hspace{0.1cm} \sum_{i=0}^{n-1}(y_i-f(x_i))^2.
|
||||
$$
|
||||
<p> <br>
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec56">Gradient Boosting, algorithm </h2>
|
||||
|
||||
<p>
|
||||
Suppose we have a cost function \( C(f)=\sum_{i=0}^{n-1}L(y_i, f(x_i)) \) where \( y_i \) is our target and \( f(x_i) \) the function which is meant to model \( y_i \). The above cost function could be our standard squared-error function
|
||||
@@ -2401,7 +2413,7 @@ The way we proceed in an iterative fashion is to
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec56">Gradient Boosting, Examples of Regression </h2>
|
||||
<h2 id="___sec57">Gradient Boosting, Examples of Regression </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
@@ -2456,7 +2468,7 @@ plt.show()
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec57">Gradient Boosting, Classification Example </h2>
|
||||
<h2 id="___sec58">Gradient Boosting, Classification Example </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
@@ -2505,7 +2517,7 @@ plt.show()
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec58">XGBoost: Extreme Gradient Boosting </h2>
|
||||
<h2 id="___sec59">XGBoost: Extreme Gradient Boosting </h2>
|
||||
|
||||
<p>
|
||||
<a href="https://github.com/dmlc/xgboost" target="_blank">XGBoost</a> or Extreme Gradient
|
||||
@@ -2526,7 +2538,7 @@ It is now the algorithm which wins essentially all ML competitions!!!
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec59">Regression Case </h2>
|
||||
<h2 id="___sec60">Regression Case </h2>
|
||||
|
||||
<p>
|
||||
|
||||
@@ -2582,7 +2594,7 @@ plt.show()
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec60">Xgboost on the Cancer Data </h2>
|
||||
<h2 id="___sec61">Xgboost on the Cancer Data </h2>
|
||||
|
||||
<p>
|
||||
As you will see from the confusion matrix below, XGBoots does an excellent job on the Wisconsin cancer data and outperforms essentially all agorithms we have discussed till now.
|
||||
|
||||
@@ -160,19 +160,26 @@ div { text-align: justify; text-justify: inter-word; }
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
('Gradient Boosting, Examples of Regression',
|
||||
2,
|
||||
None,
|
||||
'___sec56'),
|
||||
'___sec57'),
|
||||
('Gradient Boosting, Classification Example',
|
||||
2,
|
||||
None,
|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
('Regression Case', 2, None, '___sec59'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec60')]}
|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -2316,7 +2323,7 @@ plt.show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec54">Gradient boosting: Basics </h2>
|
||||
<h2 id="___sec54">Gradient boosting: Basics with Steepest Descent </h2>
|
||||
|
||||
<p>
|
||||
Gradient boosting is again a similar technique to Adaptive boosting,
|
||||
@@ -2329,12 +2336,22 @@ bringing back the essential steps in linear regression, where our cost
|
||||
function was the least squares function.
|
||||
|
||||
<p>
|
||||
See discussion during lecture November 8.
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec55">The Squared-Error again! Steepest Descent </h2>
|
||||
|
||||
<p>
|
||||
We start again with our cost function \( {\cal C}(\boldsymbol{y}m\boldsymbol{f})=\sum_{i=0}^{n-1}{\cal L}(y_i, f(x_i)) \) where we want to minimize
|
||||
This means that for every iteration, we need to optimize
|
||||
|
||||
$$
|
||||
(\hat{\boldsymbol{f}}) \mathrm{argmin}_{\boldsymbol{f}}\hspace{0.1cm} \sum_{i=0}^{n-1}(y_i-f(x_i))^2.
|
||||
$$
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec55">Gradient Boosting, algorithm </h2>
|
||||
<h2 id="___sec56">Gradient Boosting, algorithm </h2>
|
||||
|
||||
<p>
|
||||
Suppose we have a cost function \( C(f)=\sum_{i=0}^{n-1}L(y_i, f(x_i)) \) where \( y_i \) is our target and \( f(x_i) \) the function which is meant to model \( y_i \). The above cost function could be our standard squared-error function
|
||||
@@ -2360,7 +2377,7 @@ The way we proceed in an iterative fashion is to
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec56">Gradient Boosting, Examples of Regression </h2>
|
||||
<h2 id="___sec57">Gradient Boosting, Examples of Regression </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
@@ -2414,7 +2431,7 @@ plt.show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec57">Gradient Boosting, Classification Example </h2>
|
||||
<h2 id="___sec58">Gradient Boosting, Classification Example </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
|
||||
@@ -2462,7 +2479,7 @@ plt.show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec58">XGBoost: Extreme Gradient Boosting </h2>
|
||||
<h2 id="___sec59">XGBoost: Extreme Gradient Boosting </h2>
|
||||
|
||||
<p>
|
||||
<a href="https://github.com/dmlc/xgboost" target="_blank">XGBoost</a> or Extreme Gradient
|
||||
@@ -2483,7 +2500,7 @@ 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="___sec59">Regression Case </h2>
|
||||
<h2 id="___sec60">Regression Case </h2>
|
||||
|
||||
<p>
|
||||
|
||||
@@ -2538,7 +2555,7 @@ plt.show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec60">Xgboost on the Cancer Data </h2>
|
||||
<h2 id="___sec61">Xgboost on the Cancer Data </h2>
|
||||
|
||||
<p>
|
||||
As you will see from the confusion matrix below, XGBoots does an excellent job on the Wisconsin cancer data and outperforms essentially all agorithms we have discussed till now.
|
||||
|
||||
@@ -165,19 +165,26 @@ div { text-align: justify; text-justify: inter-word; }
|
||||
'___sec51'),
|
||||
('Basic Steps of AdaBoost', 2, None, '___sec52'),
|
||||
('AdaBoost Examples', 2, None, '___sec53'),
|
||||
('Gradient boosting: Basics', 2, None, '___sec54'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec55'),
|
||||
('Gradient boosting: Basics with Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('The Squared-Error again! Steepest Descent',
|
||||
2,
|
||||
None,
|
||||
'___sec55'),
|
||||
('Gradient Boosting, algorithm', 2, None, '___sec56'),
|
||||
('Gradient Boosting, Examples of Regression',
|
||||
2,
|
||||
None,
|
||||
'___sec56'),
|
||||
'___sec57'),
|
||||
('Gradient Boosting, Classification Example',
|
||||
2,
|
||||
None,
|
||||
'___sec57'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec58'),
|
||||
('Regression Case', 2, None, '___sec59'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec60')]}
|
||||
'___sec58'),
|
||||
('XGBoost: Extreme Gradient Boosting', 2, None, '___sec59'),
|
||||
('Regression Case', 2, None, '___sec60'),
|
||||
('Xgboost on the Cancer Data', 2, None, '___sec61')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -2321,7 +2328,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec54">Gradient boosting: Basics </h2>
|
||||
<h2 id="___sec54">Gradient boosting: Basics with Steepest Descent </h2>
|
||||
|
||||
<p>
|
||||
Gradient boosting is again a similar technique to Adaptive boosting,
|
||||
@@ -2334,12 +2341,22 @@ bringing back the essential steps in linear regression, where our cost
|
||||
function was the least squares function.
|
||||
|
||||
<p>
|
||||
See discussion during lecture November 8.
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec55">The Squared-Error again! Steepest Descent </h2>
|
||||
|
||||
<p>
|
||||
We start again with our cost function \( {\cal C}(\boldsymbol{y}m\boldsymbol{f})=\sum_{i=0}^{n-1}{\cal L}(y_i, f(x_i)) \) where we want to minimize
|
||||
This means that for every iteration, we need to optimize
|
||||
|
||||
$$
|
||||
(\hat{\boldsymbol{f}}) \mathrm{argmin}_{\boldsymbol{f}}\hspace{0.1cm} \sum_{i=0}^{n-1}(y_i-f(x_i))^2.
|
||||
$$
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec55">Gradient Boosting, algorithm </h2>
|
||||
<h2 id="___sec56">Gradient Boosting, algorithm </h2>
|
||||
|
||||
<p>
|
||||
Suppose we have a cost function \( C(f)=\sum_{i=0}^{n-1}L(y_i, f(x_i)) \) where \( y_i \) is our target and \( f(x_i) \) the function which is meant to model \( y_i \). The above cost function could be our standard squared-error function
|
||||
@@ -2365,7 +2382,7 @@ The way we proceed in an iterative fashion is to
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec56">Gradient Boosting, Examples of Regression </h2>
|
||||
<h2 id="___sec57">Gradient Boosting, Examples of Regression </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
@@ -2419,7 +2436,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec57">Gradient Boosting, Classification Example </h2>
|
||||
<h2 id="___sec58">Gradient Boosting, Classification Example </h2>
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
@@ -2467,7 +2484,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec58">XGBoost: Extreme Gradient Boosting </h2>
|
||||
<h2 id="___sec59">XGBoost: Extreme Gradient Boosting </h2>
|
||||
|
||||
<p>
|
||||
<a href="https://github.com/dmlc/xgboost" target="_blank">XGBoost</a> or Extreme Gradient
|
||||
@@ -2488,7 +2505,7 @@ 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="___sec59">Regression Case </h2>
|
||||
<h2 id="___sec60">Regression Case </h2>
|
||||
|
||||
<p>
|
||||
|
||||
@@ -2543,7 +2560,7 @@ plt<span style="color: #666666">.</span>show()
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec60">Xgboost on the Cancer Data </h2>
|
||||
<h2 id="___sec61">Xgboost on the Cancer Data </h2>
|
||||
|
||||
<p>
|
||||
As you will see from the confusion matrix below, XGBoots does an excellent job on the Wisconsin cancer data and outperforms essentially all agorithms we have discussed till now.
|
||||
|
||||
@@ -2555,7 +2555,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Gradient boosting: Basics\n",
|
||||
"## Gradient boosting: Basics with Steepest Descent\n",
|
||||
"\n",
|
||||
"Gradient boosting is again a similar technique to Adaptive boosting,\n",
|
||||
"it combines so-called weak classifiers or regressors into a strong\n",
|
||||
@@ -2565,8 +2565,25 @@
|
||||
"bringing back the essential steps in linear regression, where our cost\n",
|
||||
"function was the least squares function.\n",
|
||||
"\n",
|
||||
"See discussion during lecture November 8.\n",
|
||||
"## The Squared-Error again! Steepest Descent\n",
|
||||
"\n",
|
||||
"We start again with our cost function ${\\cal C}(\\boldsymbol{y}m\\boldsymbol{f})=\\sum_{i=0}^{n-1}{\\cal L}(y_i, f(x_i))$ where we want to minimize\n",
|
||||
"This means that for every iteration, we need to optimize"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$$\n",
|
||||
"(\\hat{\\boldsymbol{f}}) \\mathrm{argmin}_{\\boldsymbol{f}}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n",
|
||||
"$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Gradient Boosting, algorithm\n",
|
||||
"\n",
|
||||
"Suppose we have a cost function $C(f)=\\sum_{i=0}^{n-1}L(y_i, f(x_i))$ where $y_i$ is our target and $f(x_i)$ the function which is meant to model $y_i$. The above cost function could be our standard squared-error function"
|
||||
|
||||
Binary file not shown.
Binary file not shown.
@@ -1938,7 +1938,7 @@ plt.show()
|
||||
|
||||
|
||||
!split
|
||||
===== Gradient boosting: Basics =====
|
||||
===== Gradient boosting: Basics with Steepest Descent =====
|
||||
|
||||
Gradient boosting is again a similar technique to Adaptive boosting,
|
||||
it combines so-called weak classifiers or regressors into a strong
|
||||
@@ -1948,7 +1948,20 @@ In order to understand the method, let us illustrate its basics by
|
||||
bringing back the essential steps in linear regression, where our cost
|
||||
function was the least squares function.
|
||||
|
||||
See discussion during lecture November 8.
|
||||
!split
|
||||
===== The Squared-Error again! Steepest Descent =====
|
||||
|
||||
We start again with our cost function ${\cal C}(\bm{y}m\bm{f})=\sum_{i=0}^{n-1}{\cal L}(y_i, f(x_i))$ where we want to minimize
|
||||
This means that for every iteration, we need to optimize
|
||||
|
||||
!bt
|
||||
\[
|
||||
(\hat{\bm{f}}) \mathrm{argmin}_{\bm{f}}\hspace{0.1cm} \sum_{i=0}^{n-1}(y_i-f(x_i))^2.
|
||||
\]
|
||||
!et
|
||||
|
||||
|
||||
|
||||
|
||||
!split
|
||||
===== Gradient Boosting, algorithm =====
|
||||
|
||||
Reference in New Issue
Block a user