From cfd31bac47608ecbefb954922067ef745d4b432d Mon Sep 17 00:00:00 2001 From: mhjensen Date: Thu, 7 Nov 2019 13:51:33 +0100 Subject: [PATCH] adding xgboost code --- .../html/._DecisionTrees-bs000.html | 50 +++--- .../html/._DecisionTrees-bs001.html | 50 +++--- .../html/._DecisionTrees-bs002.html | 50 +++--- .../html/._DecisionTrees-bs003.html | 50 +++--- .../html/._DecisionTrees-bs004.html | 50 +++--- .../html/._DecisionTrees-bs005.html | 50 +++--- .../html/._DecisionTrees-bs006.html | 50 +++--- .../html/._DecisionTrees-bs007.html | 50 +++--- .../html/._DecisionTrees-bs008.html | 50 +++--- .../html/._DecisionTrees-bs009.html | 50 +++--- .../html/._DecisionTrees-bs010.html | 50 +++--- .../html/._DecisionTrees-bs011.html | 50 +++--- .../html/._DecisionTrees-bs012.html | 50 +++--- .../html/._DecisionTrees-bs013.html | 50 +++--- .../html/._DecisionTrees-bs014.html | 50 +++--- .../html/._DecisionTrees-bs015.html | 50 +++--- .../html/._DecisionTrees-bs016.html | 50 +++--- .../html/._DecisionTrees-bs017.html | 50 +++--- .../html/._DecisionTrees-bs018.html | 50 +++--- .../html/._DecisionTrees-bs019.html | 50 +++--- .../html/._DecisionTrees-bs020.html | 50 +++--- .../html/._DecisionTrees-bs021.html | 50 +++--- .../html/._DecisionTrees-bs022.html | 50 +++--- .../html/._DecisionTrees-bs023.html | 50 +++--- .../html/._DecisionTrees-bs024.html | 50 +++--- .../html/._DecisionTrees-bs025.html | 50 +++--- .../html/._DecisionTrees-bs026.html | 50 +++--- .../html/._DecisionTrees-bs027.html | 50 +++--- .../html/._DecisionTrees-bs028.html | 50 +++--- .../html/._DecisionTrees-bs029.html | 50 +++--- .../html/._DecisionTrees-bs030.html | 50 +++--- .../html/._DecisionTrees-bs031.html | 50 +++--- .../html/._DecisionTrees-bs032.html | 50 +++--- .../html/._DecisionTrees-bs033.html | 50 +++--- .../html/._DecisionTrees-bs034.html | 50 +++--- .../html/._DecisionTrees-bs035.html | 50 +++--- .../html/._DecisionTrees-bs036.html | 50 +++--- .../html/._DecisionTrees-bs037.html | 50 +++--- .../html/._DecisionTrees-bs038.html | 50 +++--- .../html/._DecisionTrees-bs039.html | 50 +++--- .../html/._DecisionTrees-bs040.html | 50 +++--- .../html/._DecisionTrees-bs041.html | 131 ++++++++------ .../html/._DecisionTrees-bs042.html | 100 ++++++----- .../html/._DecisionTrees-bs043.html | 131 +++++--------- .../html/._DecisionTrees-bs044.html | 127 +++++++++---- .../html/._DecisionTrees-bs045.html | 116 ++++-------- .../html/._DecisionTrees-bs046.html | 113 ++++++++---- .../html/._DecisionTrees-bs047.html | 73 ++++---- .../html/._DecisionTrees-bs048.html | 91 ++++------ .../html/._DecisionTrees-bs049.html | 86 ++++++--- .../html/._DecisionTrees-bs050.html | 49 ++--- .../html/._DecisionTrees-bs051.html | 49 ++--- .../html/._DecisionTrees-bs052.html | 49 ++--- .../html/._DecisionTrees-bs053.html | 49 ++--- .../DecisionTrees/html/DecisionTrees-bs.html | 50 +++--- .../html/DecisionTrees-reveal.html | 145 +++++++++++++-- .../html/DecisionTrees-solarized.html | 170 ++++++++++++++---- doc/pub/DecisionTrees/html/DecisionTrees.html | 170 ++++++++++++++---- .../DecisionTrees/ipynb/DecisionTrees.ipynb | 156 ++++++++++++++-- .../ipynb/ipynb-DecisionTrees-src.tar.gz | Bin 294061 -> 294061 bytes .../pdf/DecisionTrees-minted.pdf | Bin 526615 -> 526683 bytes doc/src/DecisionTrees/DecisionTrees.do.txt | 129 ++++++++++++- doc/src/DecisionTrees/Programs/bootstrap.py~ | 56 ++++++ .../DecisionTrees/Programs/rfbootclassify.py | 54 ++++++ .../DecisionTrees/Programs/rfbootclassify.py~ | 54 ++++++ doc/src/DecisionTrees/Programs/rfbootstrap.py | 7 +- .../DecisionTrees/Programs/rfbootstrap.py~ | 54 ++++++ doc/src/DecisionTrees/Programs/rfcancer.py | 2 - doc/src/DecisionTrees/Programs/xgcancer.py~ | 41 +++++ doc/src/DecisionTrees/Programs/xgregressor.py | 47 +++++ .../DecisionTrees/Programs/xgregressor.py~ | 47 +++++ 71 files changed, 2692 insertions(+), 1704 deletions(-) create mode 100644 doc/src/DecisionTrees/Programs/bootstrap.py~ create mode 100644 doc/src/DecisionTrees/Programs/rfbootclassify.py create mode 100644 doc/src/DecisionTrees/Programs/rfbootclassify.py~ create mode 100644 doc/src/DecisionTrees/Programs/rfbootstrap.py~ create mode 100644 doc/src/DecisionTrees/Programs/xgcancer.py~ create mode 100644 doc/src/DecisionTrees/Programs/xgregressor.py create mode 100644 doc/src/DecisionTrees/Programs/xgregressor.py~ diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs000.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs000.html index a15bd51fb..cfc8c9a63 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs000.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs000.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -283,7 +283,7 @@ MathJax.Hub.Config({
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs001.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs001.html index b2cd9fb7b..ce0cd4709 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs001.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs001.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -288,7 +288,7 @@ given some assumptions, make predictions about the target feature value
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs002.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs002.html index aa5980198..826c588f6 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs002.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs002.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -266,7 +266,7 @@ This tree was produced using the Wisconsin cancer data (discussed here as well,
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs003.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs003.html index 61fa8fbdb..7227d3372 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs003.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs003.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -274,7 +274,7 @@ node.
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs004.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs004.html index 64a653ebd..95963bdbe 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs004.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs004.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -275,7 +275,7 @@ Then we are essentially done!
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs005.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs005.html index 8c1250e07..7c89669c1 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs005.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs005.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -354,7 +354,7 @@ plt.show()
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs006.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs006.html index 16a66cb17..d66b98492 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs006.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs006.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -287,7 +287,7 @@ within box \( j \).
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs007.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs007.html index 7ce294c03..0127d1301 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs007.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs007.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -279,7 +279,7 @@ better tree in some future step.
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs008.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs008.html index 753e842e5..212fcf49e 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs008.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs008.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -312,7 +312,7 @@ region contains more than five observations.
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs009.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs009.html index cd8fc3d5f..697c502e2 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs009.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs009.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -281,7 +281,7 @@ parameter \( \alpha \).
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs010.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs010.html index 413ea449d..d255f36e7 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs010.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs010.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -294,7 +294,7 @@ subtree corresponding to \( \alpha \).
  • 19
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs011.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs011.html index 040bfb450..88e444f30 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs011.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs011.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -290,7 +290,7 @@ MathJax.Hub.Config({
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs012.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs012.html index dfd9431c2..2809ab887 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs012.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs012.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -282,7 +282,7 @@ fall into that region.
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs013.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs013.html index ec841c432..4f42c7a2b 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs013.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs013.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -287,7 +287,7 @@ than is the classification error rate.
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs014.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs014.html index 32747a5b5..02016f7be 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs014.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs014.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -313,7 +313,7 @@ $$
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs015.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs015.html index 8c075d6ca..7fd9c8230 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs015.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs015.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -304,7 +304,7 @@ os.system(cmd)
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs016.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs016.html index d10d8fe9f..8c7b4e80d 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs016.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs016.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -295,7 +295,7 @@ os.system(cmd)
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs017.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs017.html index 4099218b1..904e184ad 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs017.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs017.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -278,7 +278,7 @@ We discuss both algorithms with applications here. The popular library Scikit
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs018.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs018.html index 523dfd7a6..644fe7bbb 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs018.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs018.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -268,7 +268,7 @@ MathJax.Hub.Config({
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs019.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs019.html index 0986c5322..8d2a18b42 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs019.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs019.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -268,7 +268,7 @@ MathJax.Hub.Config({
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs020.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs020.html index f239246f8..a1a708d79 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs020.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs020.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -308,7 +308,7 @@ The table here summarizes the various attributes and
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs021.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs021.html index 83347ed34..c0c27cf60 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs021.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs021.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -339,7 +339,7 @@ os.system(cmd)
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs022.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs022.html index ae8bb366f..cf1324afa 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs022.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs022.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -341,7 +341,7 @@ split = get_split(dataset)
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs023.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs023.html index be1b6cdb0..1e3e04676 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs023.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs023.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -299,7 +299,7 @@ attributes at each step while growing the tree.
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs024.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs024.html index ea13526c7..4be1ac486 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs024.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs024.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -459,7 +459,7 @@ MathJax.Hub.Config({
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs025.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs025.html index 2dcfdb108..b6806a8e1 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs025.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs025.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -312,7 +312,7 @@ deep_tree_clf.fit(X_train_scaled, y_train)
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs026.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs026.html index c12b2aea9..7ab79cd45 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs026.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs026.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -335,7 +335,7 @@ plt.show()
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs027.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs027.html index 9a3fdcb8b..785b455fa 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs027.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs027.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -291,7 +291,7 @@ plt.show()
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs028.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs028.html index 7cb8a8a9d..7755c7d4d 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs028.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs028.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -285,7 +285,7 @@ tree_reg.fit(X, y)
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs029.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs029.html index d9412629b..ed615163d 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs029.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs029.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -341,7 +341,7 @@ plt.show()
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs030.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs030.html index 4a1e86086..f7faa8f50 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs030.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs030.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -277,7 +277,7 @@ MathJax.Hub.Config({
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs031.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs031.html index fc659ead6..5c6217b3f 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs031.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs031.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -280,7 +280,7 @@ However, by aggregating many decision trees, using methods like bagging, random
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs032.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs032.html index 256109b6c..711b9c8e6 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs032.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs032.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -287,7 +287,7 @@ We discuss these methods here.
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs033.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs033.html index 7682c9683..d1b16089b 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs033.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs033.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -271,7 +271,7 @@ MathJax.Hub.Config({
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs034.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs034.html index dacb801bf..04be98f42 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs034.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs034.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -282,7 +282,7 @@ learning method.
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs035.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs035.html index 7f0156aa2..0750e65bb 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs035.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs035.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -292,7 +292,7 @@ predictor, averaged over all \( B \) trees.
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs036.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs036.html index 02a236fc7..f234a1f54 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs036.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs036.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -283,7 +283,7 @@ plt.show()
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs037.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs037.html index 161d54c68..da7b2e0c1 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs037.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs037.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -314,7 +314,7 @@ voting_clf.fit(X_train, y_train)
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    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs038.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs038.html index 96e5c79e6..3b1d454d3 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs038.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs038.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -321,7 +321,7 @@ voting_clf.fit(X_train, y_train)
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  • »
    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs039.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs039.html index 314a928c0..c5850d1bb 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs039.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs039.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -324,7 +324,7 @@ plt.show()
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  • »
    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs040.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs040.html index e673f5578..b63ab6261 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs040.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs040.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -323,7 +323,7 @@ plt.show()
  • 49
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    • -
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  • »
    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs041.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs041.html index e51b1ff27..dbd2fe7c9 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs041.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs041.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -240,47 +240,62 @@ MathJax.Hub.Config({ -

    Random forests

    +

    Changing the Level of the Decision Tree

    -Random forests provide an improvement over bagged trees by way of a -small tweak that decorrelates the trees. -

    -As in bagging, we build a -number of decision trees on bootstrapped training samples. But when -building these decision trees, each time a split in a tree is -considered, a random sample of \( m \) predictors is chosen as split -candidates from the full set of \( p \) predictors. The split is allowed to -use only one of those \( m \) predictors. + +

    import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.utils import resample
+from sklearn.tree import DecisionTreeRegressor
 
-
    
-A fresh sample of \( m \) predictors is
-taken at each split, and typically we choose 
+n = 100
+n_boostraps = 100
+maxdepth = 8
 
-$$
-m\approx \sqrt{p}.
-$$
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+error = np.zeros(maxdepth)
+bias = np.zeros(maxdepth)
+variance = np.zeros(maxdepth)
+polydegree = np.zeros(maxdepth)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
 
-
    
-In building a random forest, at
-each split in the tree, the algorithm is not even allowed to consider
-a majority of the available predictors.
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
 
-
    
-The reason for this is rather clever. Suppose that there is one very
-strong predictor in the data set, along with a number of other
-moderately strong predictors. Then in the collection of bagged
-variable importance random forest trees, most or all of the trees will
-use this strong predictor in the top split. Consequently, all of the
-bagged trees will look quite similar to each other. Hence the
-predictions from the bagged trees will be highly correlated.
-Unfortunately, averaging many highly correlated quantities does not
-lead to as large of a reduction in variance as averaging many
-uncorrelated quanti- ties. In particular, this means that bagging will
-not lead to a substantial reduction in variance over a single tree in
-this setting.
+for degree in range(1,maxdepth):
+    model = DecisionTreeRegressor(max_depth=degree) 
+    y_pred = np.empty((y_test.shape[0], n_boostraps))
+    for i in range(n_boostraps):
+        x_, y_ = resample(X_train_scaled, y_train)
+        model.fit(x_, y_)
+        y_pred[:, i] = model.predict(X_test_scaled)#.ravel()
 
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
+    variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
+    print('Polynomial degree:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.xlim(1,maxdepth)
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+

    @@ -307,7 +322,7 @@ this setting.

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  • »
    • diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs042.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs042.html index 486a0c3b1..9483dcfa1 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs042.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs042.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -240,30 +240,48 @@ MathJax.Hub.Config({ -

    Random Forest Algorithm

    -The algorithm described here can be applied to both classification and regression problems. +

    Random forests

    -We will grow of forest of say \( M \) trees. +Random forests provide an improvement over bagged trees by way of a +small tweak that decorrelates the trees. -

      -
    1. For \( m=1:M \) we
      2. +

      +As in bagging, we build a +number of decision trees on bootstrapped training samples. But when +building these decision trees, each time a split in a tree is +considered, a random sample of \( m \) predictors is chosen as split +candidates from the full set of \( p \) predictors. The split is allowed to +use only one of those \( m \) predictors. -

        -
      • Draw a bootstrap sample of from the training data organized in our \( \boldsymbol{X} \) matrix.
        • -
      • We grow then a random forest tree \( T_m \) based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached
        • +

        +A fresh sample of \( m \) predictors is +taken at each split, and typically we choose -

          -
        1. we select \( m \le p \) varibales at random from the \( p \) predictors/features
          2. -
        2. pick the best split point among the \( m \) features using either the CART algorithm or the ID3 for classification and create a new node
          3. -
        3. split the node into daughter nodes
          4. -
        +$$ +m\approx \sqrt{p}. +$$ -
      +

      +In building a random forest, at +each split in the tree, the algorithm is not even allowed to consider +a majority of the available predictors. -

    2. Output then the ensemble of trees \( \{T_m\}_1^{M} \) and make predictions for either a regression type of problem or a classification type of problem.
      3. -
    +

    +The reason for this is rather clever. Suppose that there is one very +strong predictor in the data set, along with a number of other +moderately strong predictors. Then in the collection of bagged +variable importance random forest trees, most or all of the trees will +use this strong predictor in the top split. Consequently, all of the +bagged trees will look quite similar to each other. Hence the +predictions from the bagged trees will be highly correlated. +Unfortunately, averaging many highly correlated quantities does not +lead to as large of a reduction in variance as averaging many +uncorrelated quanti- ties. In particular, this means that bagging will +not lead to a substantial reduction in variance over a single tree in +this setting. +

    diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs043.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs043.html index c4bc06499..45f547496 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs043.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs043.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -240,77 +240,30 @@ MathJax.Hub.Config({ -

    Random Forests Compared with other Methods on the Cancer Data

    +

    Random Forest Algorithm

    +The algorithm described here can be applied to both classification and regression problems. +

    +We will grow of forest of say \( M \) trees. - -

    import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.model_selection import  train_test_split 
-from sklearn.datasets import load_breast_cancer
-from sklearn.svm import SVC
-from sklearn.linear_model import LogisticRegression
-from sklearn.tree import DecisionTreeClassifier
+
      
+
    1.  For \( m=1:M \) we
      2. 
 
-# Load the data
-cancer = load_breast_cancer()
+
        
+  
      •  Draw a bootstrap sample of from the training data organized in our \( \boldsymbol{X} \) matrix.
        • 
+  
      •  We grow then a random forest tree \( T_m \) based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached
        • 
 
-X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
-print(X_train.shape)
-print(X_test.shape)
-# Logistic Regression
-logreg = LogisticRegression(solver='lbfgs')
-logreg.fit(X_train, y_train)
-print("Test set accuracy with Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
-# Support vector machine
-svm = SVC(gamma='auto', C=100)
-svm.fit(X_train, y_train)
-print("Test set accuracy with SVM: {:.2f}".format(svm.score(X_test,y_test)))
-# Decision Trees
-deep_tree_clf = DecisionTreeClassifier(max_depth=None)
-deep_tree_clf.fit(X_train, y_train)
-print("Test set accuracy with Decision Trees: {:.2f}".format(deep_tree_clf.score(X_test,y_test)))
-#now scale the data
-from sklearn.preprocessing import StandardScaler
-scaler = StandardScaler()
-scaler.fit(X_train)
-X_train_scaled = scaler.transform(X_train)
-X_test_scaled = scaler.transform(X_test)
-# Logistic Regression
-logreg.fit(X_train_scaled, y_train)
-print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
-# Support Vector Machine
-svm.fit(X_train_scaled, y_train)
-print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
-# Decision Trees
-deep_tree_clf.fit(X_train_scaled, y_train)
-print("Test set accuracy with Decision Trees and scaled data: {:.2f}".format(deep_tree_clf.score(X_test_scaled,y_test)))
+
          
+   
        1.  we select \( m \le p \) varibales at random from the \( p \) predictors/features
          2. 
+   
        2.  pick the best split point among the \( m \) features using either the CART algorithm or the ID3 for classification and create a new node
          3. 
+   
        3.  split the node into daughter nodes
          4. 
+
        
 
+
      
 
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import cross_validate
-# Data set not specificied
-#Instantiate the model with 500 trees and entropy as splitting criteria
-Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion="entropy")
-Random_Forest_model.fit(X_train_scaled, y_train)
-#Cross validation
-accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']
-print(accuracy)
-print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(Random_Forest_model.score(X_test_scaled,y_test)))
+
    2.  Output then the ensemble of trees \( \{T_m\}_1^{M} \) and make predictions for either a regression type of problem or a classification type of problem.
      3.  
+
    
 
-
-import scikitplot as skplt
-y_pred = Random_Forest_model.predict(X_test_scaled)
-skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
-plt.show()
-y_probas = Random_Forest_model.predict_proba(X_test_scaled)
-skplt.metrics.plot_roc(y_test, y_probas)
-plt.show()
-skplt.metrics.plot_cumulative_gain(y_test, y_probas)
-plt.show()
-
    -

    diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs044.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs044.html index 2401d68f0..7e7fe255f 100644 --- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs044.html +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs044.html @@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source '___sec37'), ('Now Bagging', 2, None, '___sec38'), ('Making our own Bagging with Bootstrap', 2, None, '___sec39'), - ('Random forests', 2, None, '___sec40'), - ('Random Forest Algorithm', 2, None, '___sec41'), + ('Changing the Level of the Decision Tree', 2, None, '___sec40'), + ('Random forests', 2, None, '___sec41'), + ('Random Forest Algorithm', 2, None, '___sec42'), ('Random Forests Compared with other Methods on the Cancer Data', 2, None, - '___sec42'), + '___sec43'), ('Compare Bagging on Trees with Random Forests', 2, None, - '___sec43'), - ('Bootstrap with Random Forests Instead of a Single Tree', + '___sec44'), + ('Bootstrap with Random Forests Instead of a Single Tree, own ' + 'Bagging', 2, None, - '___sec44'), - ("Boosting, a Bird'e Eye", 2, None, '___sec45'), + '___sec45'), + ("Boosting, a Bird'e Eye", 2, None, '___sec46'), ('Adaptive boosting: AdaBoost, Basic Algorithm', 2, None, - '___sec46'), - ('Basic Steps of AdaBoost', 2, None, '___sec47'), - ('Figure to Illustrate the Iterative Classification Process', - 2, - None, - '___sec48'), + '___sec47'), + ('Basic Steps of AdaBoost', 2, None, '___sec48'), ('AdaBoost Examples', 2, None, '___sec49'), ('Gradient boosting: Basics', 2, None, '___sec50'), ('Gradient Boosting, algorithm', 2, None, '___sec51'), ('Gradient Boosting, Examples', 2, None, '___sec52'), ('Gradient Boots with Early Stopping', 2, None, '___sec53'), ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'), - ('Xgboost on the Cancer Data', 2, None, '___sec55')]} + ('Regression Case', 2, None, '___sec55'), + ('Xgboost on the Cancer Data', 2, None, '___sec56')]} end of tocinfo --> @@ -209,22 +208,23 @@ MathJax.Hub.Config({
  • Please, not the moons again! Voting and Bagging
  • Now Bagging
  • Making our own Bagging with Bootstrap
    • -
  • Random forests
    • -
  • Random Forest Algorithm
    • -
  • Random Forests Compared with other Methods on the Cancer Data
    • -
  • Compare Bagging on Trees with Random Forests
    • -
  • Bootstrap with Random Forests Instead of a Single Tree
    • -
  • Boosting, a Bird'e Eye
    • -
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • -
  • Basic Steps of AdaBoost
    • -
  • Figure to Illustrate the Iterative Classification Process
    • +
  • Changing the Level of the Decision Tree
    • +
  • Random forests
    • +
  • Random Forest Algorithm
    • +
  • Random Forests Compared with other Methods on the Cancer Data
    • +
  • Compare Bagging on Trees with Random Forests
    • +
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • +
  • Boosting, a Bird'e Eye
    • +
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • +
  • Basic Steps of AdaBoost
  • AdaBoost Examples
  • Gradient boosting: Basics
  • Gradient Boosting, algorithm
  • Gradient Boosting, Examples
  • Gradient Boots with Early Stopping
  • XGBoost: Extreme Gradient Boosting
    • -
  • Xgboost on the Cancer Data
    • +
  • Regression Case
    • +
  • Xgboost on the Cancer Data
    • @@ -240,24 +240,75 @@ MathJax.Hub.Config({ -

    Compare Bagging on Trees with Random Forests

    +

    Random Forests Compared with other Methods on the Cancer Data

    -

    bag_clf = BaggingClassifier(
-    DecisionTreeClassifier(splitter="random", max_leaf_nodes=16, random_state=42),
-    n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)
-
    -

    +

    import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import  train_test_split 
+from sklearn.datasets import load_breast_cancer
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+
+# Load the data
+cancer = load_breast_cancer()
+
+X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
+print(X_train.shape)
+print(X_test.shape)
+# Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
+logreg.fit(X_train, y_train)
+print("Test set accuracy with Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
+# Support vector machine
+svm = SVC(gamma='auto', C=100)
+svm.fit(X_train, y_train)
+print("Test set accuracy with SVM: {:.2f}".format(svm.score(X_test,y_test)))
+# Decision Trees
+deep_tree_clf = DecisionTreeClassifier(max_depth=None)
+deep_tree_clf.fit(X_train, y_train)
+print("Test set accuracy with Decision Trees: {:.2f}".format(deep_tree_clf.score(X_test,y_test)))
+#now scale the data
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+# Logistic Regression
+logreg.fit(X_train_scaled, y_train)
+print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+# Support Vector Machine
+svm.fit(X_train_scaled, y_train)
+print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+# Decision Trees
+deep_tree_clf.fit(X_train_scaled, y_train)
+print("Test set accuracy with Decision Trees and scaled data: {:.2f}".format(deep_tree_clf.score(X_test_scaled,y_test)))
+
 
-
-
    bag_clf.fit(X_train, y_train)
-y_pred = bag_clf.predict(X_test)
 from sklearn.ensemble import RandomForestClassifier
-rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)
-rnd_clf.fit(X_train, y_train)
-y_pred_rf = rnd_clf.predict(X_test)
-np.sum(y_pred == y_pred_rf) / len(y_pred) 
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+# Data set not specificied
+#Instantiate the model with 500 trees and entropy as splitting criteria
+Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion="entropy")
+Random_Forest_model.fit(X_train_scaled, y_train)
+#Cross validation
+accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']
+print(accuracy)
+print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(Random_Forest_model.score(X_test_scaled,y_test)))
+
+
+import scikitplot as skplt
+y_pred = Random_Forest_model.predict(X_test_scaled)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
+plt.show()
+y_probas = Random_Forest_model.predict_proba(X_test_scaled)
+skplt.metrics.plot_roc(y_test, y_probas)
+plt.show()
+skplt.metrics.plot_cumulative_gain(y_test, y_probas)
+plt.show()
 
    
 
    
 
    
@@ -285,7 +336,7 @@ np.sum(y_pred =
   
  • 53
    • 
   
  • 54
    • 
   
  • ...
    • 
-  
  • 57
    • 
+  
  • 58
    • 
   
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    • 
 
 
diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs045.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs045.html
index 1431d4eb5..4372127c9 100644
--- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs045.html
+++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs045.html
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
                '___sec37'),
               ('Now Bagging', 2, None, '___sec38'),
               ('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
-              ('Random forests', 2, None, '___sec40'),
-              ('Random Forest Algorithm', 2, None, '___sec41'),
+              ('Changing the Level of the Decision Tree', 2, None, '___sec40'),
+              ('Random forests', 2, None, '___sec41'),
+              ('Random Forest Algorithm', 2, None, '___sec42'),
               ('Random Forests Compared with other Methods on the Cancer Data',
                2,
                None,
-               '___sec42'),
+               '___sec43'),
               ('Compare  Bagging on Trees with Random Forests',
                2,
                None,
-               '___sec43'),
-              ('Bootstrap with Random Forests Instead of a Single Tree',
+               '___sec44'),
+              ('Bootstrap with Random Forests Instead of a Single Tree, own '
+               'Bagging',
                2,
                None,
-               '___sec44'),
-              ("Boosting, a Bird'e Eye", 2, None, '___sec45'),
+               '___sec45'),
+              ("Boosting, a Bird'e Eye", 2, None, '___sec46'),
               ('Adaptive boosting: AdaBoost, Basic Algorithm',
                2,
                None,
-               '___sec46'),
-              ('Basic Steps of AdaBoost', 2, None, '___sec47'),
-              ('Figure to Illustrate the Iterative Classification Process',
-               2,
-               None,
-               '___sec48'),
+               '___sec47'),
+              ('Basic Steps of AdaBoost', 2, None, '___sec48'),
               ('AdaBoost Examples', 2, None, '___sec49'),
               ('Gradient boosting: Basics', 2, None, '___sec50'),
               ('Gradient Boosting, algorithm', 2, None, '___sec51'),
               ('Gradient Boosting, Examples', 2, None, '___sec52'),
               ('Gradient Boots with Early Stopping', 2, None, '___sec53'),
               ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
-              ('Xgboost on the Cancer Data', 2, None, '___sec55')]}
+              ('Regression Case', 2, None, '___sec55'),
+              ('Xgboost on the Cancer Data', 2, None, '___sec56')]}
 end of tocinfo -->
 
 
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
       
  • Please, not the moons again! Voting and Bagging
    • 
       
  • Now Bagging
    • 
       
  • Making our own Bagging with Bootstrap
    • 
-      
  • Random forests
    • 
-      
  • Random Forest Algorithm
    • 
-      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
-      
  • Compare  Bagging on Trees with Random Forests
    • 
-      
  • Bootstrap with Random Forests Instead of a Single Tree
    • 
-      
  • Boosting, a Bird'e Eye
    • 
-      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
-      
  • Basic Steps of AdaBoost
    • 
-      
  • Figure to Illustrate the Iterative Classification Process
    • 
+      
  • Changing the Level of the Decision Tree
    • 
+      
  • Random forests
    • 
+      
  • Random Forest Algorithm
    • 
+      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
+      
  • Compare  Bagging on Trees with Random Forests
    • 
+      
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • 
+      
  • Boosting, a Bird'e Eye
    • 
+      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
+      
  • Basic Steps of AdaBoost
    • 
       
  • AdaBoost Examples
    • 
       
  • Gradient boosting: Basics
    • 
       
  • Gradient Boosting, algorithm
    • 
       
  • Gradient Boosting, Examples
    • 
       
  • Gradient Boots with Early Stopping
    • 
       
  • XGBoost: Extreme Gradient Boosting
    • 
-      
  • Xgboost on the Cancer Data
    • 
+      
  • Regression Case
    • 
+      
  • Xgboost on the Cancer Data
    • 
 
         
       
@@ -240,62 +240,24 @@ MathJax.Hub.Config({
 
 
 
-
    Bootstrap with Random Forests Instead of a Single Tree 
    
-
+
    Compare  Bagging on Trees with Random Forests  
    
 
    
 
 
-
    import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.model_selection import train_test_split
-from sklearn.pipeline import make_pipeline
-from sklearn.utils import resample
-from sklearn.ensemble import RandomForestRegressor
+
    bag_clf = BaggingClassifier(
+    DecisionTreeClassifier(splitter="random", max_leaf_nodes=16, random_state=42),
+    n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)
+
    
+
    
 
-np.random.seed(2018)
-
-n = 40
-n_boostraps = 100
-maxdegree = 14
-
-# Make data set.
-x = np.linspace(-3, 3, n).reshape(-1, 1)
-y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
-error = np.zeros(maxdegree)
-bias = np.zeros(maxdegree)
-variance = np.zeros(maxdegree)
-polydegree = np.zeros(maxdegree)
-X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
-
-from sklearn.preprocessing import StandardScaler
-scaler = StandardScaler()
-scaler.fit(X_train)
-X_train_scaled = scaler.transform(X_train)
-X_test_scaled = scaler.transform(X_test)
-
-for degree in range(maxdegree):
-    model = RandomForestRegressor()
-    y_pred = np.empty((y_test.shape[0], n_boostraps))
-    for i in range(n_boostraps):
-        x_, y_ = resample(X_train_scaled, y_train)
-        model.fit(x_, y_)
-        y_pred[:, i] = model.predict(X_test_scaled).ravel()
-
-    polydegree[degree] = degree
-    error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
-    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
-    variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
-    print('Polynomial degree:', degree)
-    print('Error:', error[degree])
-    print('Bias^2:', bias[degree])
-    print('Var:', variance[degree])
-    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
-
-plt.plot(polydegree, error, label='Error')
-plt.plot(polydegree, bias, label='bias')
-plt.plot(polydegree, variance, label='Variance')
-plt.legend()
-plt.show()
+
+
    bag_clf.fit(X_train, y_train)
+y_pred = bag_clf.predict(X_test)
+from sklearn.ensemble import RandomForestClassifier
+rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)
+rnd_clf.fit(X_train, y_train)
+y_pred_rf = rnd_clf.predict(X_test)
+np.sum(y_pred == y_pred_rf) / len(y_pred) 
 
    
 
    
 
    
@@ -323,7 +285,7 @@ plt.show()
   
  • 54
    • 
   
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    • 
   
  • ...
    • 
-  
  • 57
    • 
+  
  • 58
    • 
   
  • »
    • 
 
 
diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs046.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs046.html
index 78f3f81a4..09ecd3191 100644
--- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs046.html
+++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs046.html
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
                '___sec37'),
               ('Now Bagging', 2, None, '___sec38'),
               ('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
-              ('Random forests', 2, None, '___sec40'),
-              ('Random Forest Algorithm', 2, None, '___sec41'),
+              ('Changing the Level of the Decision Tree', 2, None, '___sec40'),
+              ('Random forests', 2, None, '___sec41'),
+              ('Random Forest Algorithm', 2, None, '___sec42'),
               ('Random Forests Compared with other Methods on the Cancer Data',
                2,
                None,
-               '___sec42'),
+               '___sec43'),
               ('Compare  Bagging on Trees with Random Forests',
                2,
                None,
-               '___sec43'),
-              ('Bootstrap with Random Forests Instead of a Single Tree',
+               '___sec44'),
+              ('Bootstrap with Random Forests Instead of a Single Tree, own '
+               'Bagging',
                2,
                None,
-               '___sec44'),
-              ("Boosting, a Bird'e Eye", 2, None, '___sec45'),
+               '___sec45'),
+              ("Boosting, a Bird'e Eye", 2, None, '___sec46'),
               ('Adaptive boosting: AdaBoost, Basic Algorithm',
                2,
                None,
-               '___sec46'),
-              ('Basic Steps of AdaBoost', 2, None, '___sec47'),
-              ('Figure to Illustrate the Iterative Classification Process',
-               2,
-               None,
-               '___sec48'),
+               '___sec47'),
+              ('Basic Steps of AdaBoost', 2, None, '___sec48'),
               ('AdaBoost Examples', 2, None, '___sec49'),
               ('Gradient boosting: Basics', 2, None, '___sec50'),
               ('Gradient Boosting, algorithm', 2, None, '___sec51'),
               ('Gradient Boosting, Examples', 2, None, '___sec52'),
               ('Gradient Boots with Early Stopping', 2, None, '___sec53'),
               ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
-              ('Xgboost on the Cancer Data', 2, None, '___sec55')]}
+              ('Regression Case', 2, None, '___sec55'),
+              ('Xgboost on the Cancer Data', 2, None, '___sec56')]}
 end of tocinfo -->
 
 
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
       
  • Please, not the moons again! Voting and Bagging
    • 
       
  • Now Bagging
    • 
       
  • Making our own Bagging with Bootstrap
    • 
-      
  • Random forests
    • 
-      
  • Random Forest Algorithm
    • 
-      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
-      
  • Compare  Bagging on Trees with Random Forests
    • 
-      
  • Bootstrap with Random Forests Instead of a Single Tree
    • 
-      
  • Boosting, a Bird'e Eye
    • 
-      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
-      
  • Basic Steps of AdaBoost
    • 
-      
  • Figure to Illustrate the Iterative Classification Process
    • 
+      
  • Changing the Level of the Decision Tree
    • 
+      
  • Random forests
    • 
+      
  • Random Forest Algorithm
    • 
+      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
+      
  • Compare  Bagging on Trees with Random Forests
    • 
+      
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • 
+      
  • Boosting, a Bird'e Eye
    • 
+      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
+      
  • Basic Steps of AdaBoost
    • 
       
  • AdaBoost Examples
    • 
       
  • Gradient boosting: Basics
    • 
       
  • Gradient Boosting, algorithm
    • 
       
  • Gradient Boosting, Examples
    • 
       
  • Gradient Boots with Early Stopping
    • 
       
  • XGBoost: Extreme Gradient Boosting
    • 
-      
  • Xgboost on the Cancer Data
    • 
+      
  • Regression Case
    • 
+      
  • Xgboost on the Cancer Data
    • 
 
         
       
@@ -240,20 +240,63 @@ MathJax.Hub.Config({
 
 
 
-
    Boosting, a Bird'e Eye 
    
+
    Bootstrap with Random Forests Instead of a Single Tree, own Bagging 
    
 
 
    
-The basic idea is to combine weak classifiers in order to create a good
-classifier. With a weak classifier we often intend a classifier which
-produces results which are only slightly better than we would get by
-random guesses.
 
-
    
-This is done by applying in an iterative way a weak (or a standard
-classifier like decision trees) to modify the data. In each iteration
-we emphasize those observations which are misclassified by weighting
-them with a factor.
+
+
    import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.utils import resample
+from sklearn.ensemble import RandomForestRegressor
 
+np.random.seed(2018)
+
+n = 100
+n_boostraps = 100
+maxdegree = 14
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+error = np.zeros(maxdegree)
+bias = np.zeros(maxdegree)
+variance = np.zeros(maxdegree)
+polydegree = np.zeros(maxdegree)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(maxdegree):
+    model = RandomForestRegressor()
+    y_pred = np.empty((y_test.shape[0], n_boostraps))
+    for i in range(n_boostraps):
+        x_, y_ = resample(X_train_scaled, y_train)
+        model.fit(x_, y_.ravel())
+        y_pred[:, i] = model.predict(X_test_scaled).ravel()
+
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
+    variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
+    print('Polynomial degree:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
    
 
    
 
    
 
@@ -280,7 +323,7 @@ them with a factor.
   
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-  
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diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs047.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs047.html
index 6cddc74f8..2c10930ed 100644
--- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs047.html
+++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs047.html
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
                '___sec37'),
               ('Now Bagging', 2, None, '___sec38'),
               ('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
-              ('Random forests', 2, None, '___sec40'),
-              ('Random Forest Algorithm', 2, None, '___sec41'),
+              ('Changing the Level of the Decision Tree', 2, None, '___sec40'),
+              ('Random forests', 2, None, '___sec41'),
+              ('Random Forest Algorithm', 2, None, '___sec42'),
               ('Random Forests Compared with other Methods on the Cancer Data',
                2,
                None,
-               '___sec42'),
+               '___sec43'),
               ('Compare  Bagging on Trees with Random Forests',
                2,
                None,
-               '___sec43'),
-              ('Bootstrap with Random Forests Instead of a Single Tree',
+               '___sec44'),
+              ('Bootstrap with Random Forests Instead of a Single Tree, own '
+               'Bagging',
                2,
                None,
-               '___sec44'),
-              ("Boosting, a Bird'e Eye", 2, None, '___sec45'),
+               '___sec45'),
+              ("Boosting, a Bird'e Eye", 2, None, '___sec46'),
               ('Adaptive boosting: AdaBoost, Basic Algorithm',
                2,
                None,
-               '___sec46'),
-              ('Basic Steps of AdaBoost', 2, None, '___sec47'),
-              ('Figure to Illustrate the Iterative Classification Process',
-               2,
-               None,
-               '___sec48'),
+               '___sec47'),
+              ('Basic Steps of AdaBoost', 2, None, '___sec48'),
               ('AdaBoost Examples', 2, None, '___sec49'),
               ('Gradient boosting: Basics', 2, None, '___sec50'),
               ('Gradient Boosting, algorithm', 2, None, '___sec51'),
               ('Gradient Boosting, Examples', 2, None, '___sec52'),
               ('Gradient Boots with Early Stopping', 2, None, '___sec53'),
               ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
-              ('Xgboost on the Cancer Data', 2, None, '___sec55')]}
+              ('Regression Case', 2, None, '___sec55'),
+              ('Xgboost on the Cancer Data', 2, None, '___sec56')]}
 end of tocinfo -->
 
 
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
       
  • Please, not the moons again! Voting and Bagging
    • 
       
  • Now Bagging
    • 
       
  • Making our own Bagging with Bootstrap
    • 
-      
  • Random forests
    • 
-      
  • Random Forest Algorithm
    • 
-      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
-      
  • Compare  Bagging on Trees with Random Forests
    • 
-      
  • Bootstrap with Random Forests Instead of a Single Tree
    • 
-      
  • Boosting, a Bird'e Eye
    • 
-      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
-      
  • Basic Steps of AdaBoost
    • 
-      
  • Figure to Illustrate the Iterative Classification Process
    • 
+      
  • Changing the Level of the Decision Tree
    • 
+      
  • Random forests
    • 
+      
  • Random Forest Algorithm
    • 
+      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
+      
  • Compare  Bagging on Trees with Random Forests
    • 
+      
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • 
+      
  • Boosting, a Bird'e Eye
    • 
+      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
+      
  • Basic Steps of AdaBoost
    • 
       
  • AdaBoost Examples
    • 
       
  • Gradient boosting: Basics
    • 
       
  • Gradient Boosting, algorithm
    • 
       
  • Gradient Boosting, Examples
    • 
       
  • Gradient Boots with Early Stopping
    • 
       
  • XGBoost: Extreme Gradient Boosting
    • 
-      
  • Xgboost on the Cancer Data
    • 
+      
  • Regression Case
    • 
+      
  • Xgboost on the Cancer Data
    • 
 
         
       
@@ -240,24 +240,19 @@ MathJax.Hub.Config({
 
 
 
-
    Adaptive boosting: AdaBoost, Basic Algorithm 
    
+
    Boosting, a Bird'e Eye 
    
 
 
    
-The algorithm here is rather straightforward. Assume that our weak
-classifier is a decision tree and we consider a binary set of outputs
-with \( y_i \in \{-1,1\} \) and \( i=0,1,2,\dots,n-1 \) as our set of
-observations. Our design matrix is given in terms of the
-feature/predictor vectors
-\( \boldsymbol{X}=[\boldsymbol{x}_0\boldsymbol{x}_1\dots\boldsymbol{x}_{p-1} \). Finally, we define also a
-classifier determined by our data via a function \( G(\boldsymbol{X}) \). This function tells us how well we are able to classify our outputs/targets \( \boldsymbol{y} \).
+The basic idea is to combine weak classifiers in order to create a good
+classifier. With a weak classifier we often intend a classifier which
+produces results which are only slightly better than we would get by
+random guesses.
 
 
    
-We can then define the misclassification error \( \mathrm{err} \) as
-$$
-\mathrm{err}=\frac{1}{n}\sum_{i=0}^{n-1}I(y_i\ne G(\boldsymbol{X}_{i*}),
-$$
-
-where the function \( I() \) is one if we misclassify and zero if we classify correctly.
+This is done by applying in an iterative way a weak (or a standard
+classifier like decision trees) to modify the data. In each iteration
+we emphasize those observations which are misclassified by weighting
+them with a factor.
 
 
    
 
    
@@ -284,6 +279,8 @@ where the function \( I() \) is one if we misclassify and zero if we classify co
   
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    • 
   
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+  
  • ...
    • 
+  
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    • 
 
 
diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs048.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs048.html
index e13cc85c8..633771aec 100644
--- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs048.html
+++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs048.html
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
                '___sec37'),
               ('Now Bagging', 2, None, '___sec38'),
               ('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
-              ('Random forests', 2, None, '___sec40'),
-              ('Random Forest Algorithm', 2, None, '___sec41'),
+              ('Changing the Level of the Decision Tree', 2, None, '___sec40'),
+              ('Random forests', 2, None, '___sec41'),
+              ('Random Forest Algorithm', 2, None, '___sec42'),
               ('Random Forests Compared with other Methods on the Cancer Data',
                2,
                None,
-               '___sec42'),
+               '___sec43'),
               ('Compare  Bagging on Trees with Random Forests',
                2,
                None,
-               '___sec43'),
-              ('Bootstrap with Random Forests Instead of a Single Tree',
+               '___sec44'),
+              ('Bootstrap with Random Forests Instead of a Single Tree, own '
+               'Bagging',
                2,
                None,
-               '___sec44'),
-              ("Boosting, a Bird'e Eye", 2, None, '___sec45'),
+               '___sec45'),
+              ("Boosting, a Bird'e Eye", 2, None, '___sec46'),
               ('Adaptive boosting: AdaBoost, Basic Algorithm',
                2,
                None,
-               '___sec46'),
-              ('Basic Steps of AdaBoost', 2, None, '___sec47'),
-              ('Figure to Illustrate the Iterative Classification Process',
-               2,
-               None,
-               '___sec48'),
+               '___sec47'),
+              ('Basic Steps of AdaBoost', 2, None, '___sec48'),
               ('AdaBoost Examples', 2, None, '___sec49'),
               ('Gradient boosting: Basics', 2, None, '___sec50'),
               ('Gradient Boosting, algorithm', 2, None, '___sec51'),
               ('Gradient Boosting, Examples', 2, None, '___sec52'),
               ('Gradient Boots with Early Stopping', 2, None, '___sec53'),
               ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
-              ('Xgboost on the Cancer Data', 2, None, '___sec55')]}
+              ('Regression Case', 2, None, '___sec55'),
+              ('Xgboost on the Cancer Data', 2, None, '___sec56')]}
 end of tocinfo -->
 
 
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
       
  • Please, not the moons again! Voting and Bagging
    • 
       
  • Now Bagging
    • 
       
  • Making our own Bagging with Bootstrap
    • 
-      
  • Random forests
    • 
-      
  • Random Forest Algorithm
    • 
-      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
-      
  • Compare  Bagging on Trees with Random Forests
    • 
-      
  • Bootstrap with Random Forests Instead of a Single Tree
    • 
-      
  • Boosting, a Bird'e Eye
    • 
-      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
-      
  • Basic Steps of AdaBoost
    • 
-      
  • Figure to Illustrate the Iterative Classification Process
    • 
+      
  • Changing the Level of the Decision Tree
    • 
+      
  • Random forests
    • 
+      
  • Random Forest Algorithm
    • 
+      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
+      
  • Compare  Bagging on Trees with Random Forests
    • 
+      
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • 
+      
  • Boosting, a Bird'e Eye
    • 
+      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
+      
  • Basic Steps of AdaBoost
    • 
       
  • AdaBoost Examples
    • 
       
  • Gradient boosting: Basics
    • 
       
  • Gradient Boosting, algorithm
    • 
       
  • Gradient Boosting, Examples
    • 
       
  • Gradient Boots with Early Stopping
    • 
       
  • XGBoost: Extreme Gradient Boosting
    • 
-      
  • Xgboost on the Cancer Data
    • 
+      
  • Regression Case
    • 
+      
  • Xgboost on the Cancer Data
    • 
 
         
       
@@ -240,42 +240,24 @@ MathJax.Hub.Config({
 
 
 
-
    Basic Steps of AdaBoost 
    
+
    Adaptive boosting: AdaBoost, Basic Algorithm 
    
 
 
    
-With the above definitions we are now ready to set up the algorithm for AdaBoost.
-The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.
-
-
      
-
    1.  We start by initializing all weights to \( w_i = 1/n \), with \( i=0,1,2,\dots n-1 \). It is to see then that \( \sum_{i=0}^{n-1}w_i = 1 \).
      2. 
-
    2.  We rewrite the misclassification error as
      3.  
-
    
+The algorithm here is rather straightforward. Assume that our weak
+classifier is a decision tree and we consider a binary set of outputs
+with \( y_i \in \{-1,1\} \) and \( i=0,1,2,\dots,n-1 \) as our set of
+observations. Our design matrix is given in terms of the
+feature/predictor vectors
+\( \boldsymbol{X}=[\boldsymbol{x}_0\boldsymbol{x}_1\dots\boldsymbol{x}_{p-1} \). Finally, we define also a
+classifier determined by our data via a function \( G(\boldsymbol{X}) \). This function tells us how well we are able to classify our outputs/targets \( \boldsymbol{y} \).
 
+
    
+We can then define the misclassification error \( \mathrm{err} \) as
 $$
-\mathrm{err}=\frac{\sum_{i=0}^{n-1}w_iI(y_i\ne G(\boldsymbol{X}_{i*})}{\sum_{i=0}^{n-1}w_i},
+\mathrm{err}=\frac{1}{n}\sum_{i=0}^{n-1}I(y_i\ne G(\boldsymbol{X}_{i*}),
 $$
 
-
-
      
-
    1.  Then we start looping over all attempts at classifying, namely we start an iterative process for \( m=1:M \), where \( M \) is the final number of classifications. Our given classifier could for example be a plain decision tree.
-
-
        
- 
      1.  Fit thus a given classifier to the training using the weights \( w_i \).
        2. 
- 
      2.  Compute then \( \mathrm{err} \) and figure out which events are classified properly and which are classified wrongly.
        3. 
- 
      3.  Define a quantity $\alpha_{m} = \log{(1-\mathrm{err})/\mathrm{err}}
        4. 
- 
      4.  Set the new weights to $w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*})}.
        5. 
-
      
-
-
    2.  Compute the new classifier $G(\boldsymbol{X})= \sum_{i=0}^{n-1}\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*}).
      3. 
-
    
-
-For the iterations with \( m \le 2 \) the weights are modified
-individually at each steps. The obersvations which were misclassified
-at iteration \( m-1 \) have a weight which is larger than those which were
-classified properly. As this proceeds, the observations which were
-difficult to classifiy correctly are given a larger influence. Each
-new classification step \( m \) is then forced to concentrate on those
-observations that are missed in the previous iterations.
+where the function \( I() \) is one if we misclassify and zero if we classify correctly.
 
 
    
 
    
@@ -301,6 +283,7 @@ observations that are missed in the previous iterations.
   
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diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs049.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs049.html
index 4a6ad8a24..0a56c63ac 100644
--- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs049.html
+++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs049.html
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
                '___sec37'),
               ('Now Bagging', 2, None, '___sec38'),
               ('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
-              ('Random forests', 2, None, '___sec40'),
-              ('Random Forest Algorithm', 2, None, '___sec41'),
+              ('Changing the Level of the Decision Tree', 2, None, '___sec40'),
+              ('Random forests', 2, None, '___sec41'),
+              ('Random Forest Algorithm', 2, None, '___sec42'),
               ('Random Forests Compared with other Methods on the Cancer Data',
                2,
                None,
-               '___sec42'),
+               '___sec43'),
               ('Compare  Bagging on Trees with Random Forests',
                2,
                None,
-               '___sec43'),
-              ('Bootstrap with Random Forests Instead of a Single Tree',
+               '___sec44'),
+              ('Bootstrap with Random Forests Instead of a Single Tree, own '
+               'Bagging',
                2,
                None,
-               '___sec44'),
-              ("Boosting, a Bird'e Eye", 2, None, '___sec45'),
+               '___sec45'),
+              ("Boosting, a Bird'e Eye", 2, None, '___sec46'),
               ('Adaptive boosting: AdaBoost, Basic Algorithm',
                2,
                None,
-               '___sec46'),
-              ('Basic Steps of AdaBoost', 2, None, '___sec47'),
-              ('Figure to Illustrate the Iterative Classification Process',
-               2,
-               None,
-               '___sec48'),
+               '___sec47'),
+              ('Basic Steps of AdaBoost', 2, None, '___sec48'),
               ('AdaBoost Examples', 2, None, '___sec49'),
               ('Gradient boosting: Basics', 2, None, '___sec50'),
               ('Gradient Boosting, algorithm', 2, None, '___sec51'),
               ('Gradient Boosting, Examples', 2, None, '___sec52'),
               ('Gradient Boots with Early Stopping', 2, None, '___sec53'),
               ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
-              ('Xgboost on the Cancer Data', 2, None, '___sec55')]}
+              ('Regression Case', 2, None, '___sec55'),
+              ('Xgboost on the Cancer Data', 2, None, '___sec56')]}
 end of tocinfo -->
 
 
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
       
  • Please, not the moons again! Voting and Bagging
    • 
       
  • Now Bagging
    • 
       
  • Making our own Bagging with Bootstrap
    • 
-      
  • Random forests
    • 
-      
  • Random Forest Algorithm
    • 
-      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
-      
  • Compare  Bagging on Trees with Random Forests
    • 
-      
  • Bootstrap with Random Forests Instead of a Single Tree
    • 
-      
  • Boosting, a Bird'e Eye
    • 
-      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
-      
  • Basic Steps of AdaBoost
    • 
-      
  • Figure to Illustrate the Iterative Classification Process
    • 
+      
  • Changing the Level of the Decision Tree
    • 
+      
  • Random forests
    • 
+      
  • Random Forest Algorithm
    • 
+      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
+      
  • Compare  Bagging on Trees with Random Forests
    • 
+      
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • 
+      
  • Boosting, a Bird'e Eye
    • 
+      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
+      
  • Basic Steps of AdaBoost
    • 
       
  • AdaBoost Examples
    • 
       
  • Gradient boosting: Basics
    • 
       
  • Gradient Boosting, algorithm
    • 
       
  • Gradient Boosting, Examples
    • 
       
  • Gradient Boots with Early Stopping
    • 
       
  • XGBoost: Extreme Gradient Boosting
    • 
-      
  • Xgboost on the Cancer Data
    • 
+      
  • Regression Case
    • 
+      
  • Xgboost on the Cancer Data
    • 
 
         
       
@@ -240,7 +240,42 @@ MathJax.Hub.Config({
 
 
 
-
    Figure to Illustrate the Iterative Classification Process 
    
+
    Basic Steps of AdaBoost 
    
+
+
    
+With the above definitions we are now ready to set up the algorithm for AdaBoost.
+The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.
+
+
      
+
    1.  We start by initializing all weights to \( w_i = 1/n \), with \( i=0,1,2,\dots n-1 \). It is to see then that \( \sum_{i=0}^{n-1}w_i = 1 \).
      2. 
+
    2.  We rewrite the misclassification error as
      3.  
+
    
+
+$$
+\mathrm{err}=\frac{\sum_{i=0}^{n-1}w_iI(y_i\ne G(\boldsymbol{X}_{i*})}{\sum_{i=0}^{n-1}w_i},
+$$
+
+
+
      
+
    1.  Then we start looping over all attempts at classifying, namely we start an iterative process for \( m=1:M \), where \( M \) is the final number of classifications. Our given classifier could for example be a plain decision tree.
+
+
        
+ 
      1.  Fit then a given classifier to the training using the weights \( w_i \).
        2. 
+ 
      2.  Compute then \( \mathrm{err} \) and figure out which events are classified properly and which are classified wrongly.
        3. 
+ 
      3.  Define a quantity $\alpha_{m} = \log{(1-\mathrm{err})/\mathrm{err}}
        4. 
+ 
      4.  Set the new weights to $w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*})}.
        5. 
+
      
+
+
    2.  Compute the new classifier $G(\boldsymbol{X})= \sum_{i=0}^{n-1}\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*}).
      3. 
+
    
+
+For the iterations with \( m \le 2 \) the weights are modified
+individually at each steps. The obersvations which were misclassified
+at iteration \( m-1 \) have a weight which is larger than those which were
+classified properly. As this proceeds, the observations which were
+difficult to classifiy correctly are given a larger influence. Each
+new classification step \( m \) is then forced to concentrate on those
+observations that are missed in the previous iterations.
 
 
    
 
    
@@ -265,6 +300,7 @@ MathJax.Hub.Config({
   
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    • 
   
  • 56
    • 
   
  • 57
    • 
+  
  • 58
    • 
   
  • »
    • 
 
 
diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs050.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs050.html
index 4d7d6c3fa..c12d3cb33 100644
--- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs050.html
+++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs050.html
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
                '___sec37'),
               ('Now Bagging', 2, None, '___sec38'),
               ('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
-              ('Random forests', 2, None, '___sec40'),
-              ('Random Forest Algorithm', 2, None, '___sec41'),
+              ('Changing the Level of the Decision Tree', 2, None, '___sec40'),
+              ('Random forests', 2, None, '___sec41'),
+              ('Random Forest Algorithm', 2, None, '___sec42'),
               ('Random Forests Compared with other Methods on the Cancer Data',
                2,
                None,
-               '___sec42'),
+               '___sec43'),
               ('Compare  Bagging on Trees with Random Forests',
                2,
                None,
-               '___sec43'),
-              ('Bootstrap with Random Forests Instead of a Single Tree',
+               '___sec44'),
+              ('Bootstrap with Random Forests Instead of a Single Tree, own '
+               'Bagging',
                2,
                None,
-               '___sec44'),
-              ("Boosting, a Bird'e Eye", 2, None, '___sec45'),
+               '___sec45'),
+              ("Boosting, a Bird'e Eye", 2, None, '___sec46'),
               ('Adaptive boosting: AdaBoost, Basic Algorithm',
                2,
                None,
-               '___sec46'),
-              ('Basic Steps of AdaBoost', 2, None, '___sec47'),
-              ('Figure to Illustrate the Iterative Classification Process',
-               2,
-               None,
-               '___sec48'),
+               '___sec47'),
+              ('Basic Steps of AdaBoost', 2, None, '___sec48'),
               ('AdaBoost Examples', 2, None, '___sec49'),
               ('Gradient boosting: Basics', 2, None, '___sec50'),
               ('Gradient Boosting, algorithm', 2, None, '___sec51'),
               ('Gradient Boosting, Examples', 2, None, '___sec52'),
               ('Gradient Boots with Early Stopping', 2, None, '___sec53'),
               ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
-              ('Xgboost on the Cancer Data', 2, None, '___sec55')]}
+              ('Regression Case', 2, None, '___sec55'),
+              ('Xgboost on the Cancer Data', 2, None, '___sec56')]}
 end of tocinfo -->
 
 
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
       
  • Please, not the moons again! Voting and Bagging
    • 
       
  • Now Bagging
    • 
       
  • Making our own Bagging with Bootstrap
    • 
-      
  • Random forests
    • 
-      
  • Random Forest Algorithm
    • 
-      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
-      
  • Compare  Bagging on Trees with Random Forests
    • 
-      
  • Bootstrap with Random Forests Instead of a Single Tree
    • 
-      
  • Boosting, a Bird'e Eye
    • 
-      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
-      
  • Basic Steps of AdaBoost
    • 
-      
  • Figure to Illustrate the Iterative Classification Process
    • 
+      
  • Changing the Level of the Decision Tree
    • 
+      
  • Random forests
    • 
+      
  • Random Forest Algorithm
    • 
+      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
+      
  • Compare  Bagging on Trees with Random Forests
    • 
+      
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • 
+      
  • Boosting, a Bird'e Eye
    • 
+      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
+      
  • Basic Steps of AdaBoost
    • 
       
  • AdaBoost Examples
    • 
       
  • Gradient boosting: Basics
    • 
       
  • Gradient Boosting, algorithm
    • 
       
  • Gradient Boosting, Examples
    • 
       
  • Gradient Boots with Early Stopping
    • 
       
  • XGBoost: Extreme Gradient Boosting
    • 
-      
  • Xgboost on the Cancer Data
    • 
+      
  • Regression Case
    • 
+      
  • Xgboost on the Cancer Data
    • 
 
         
       
@@ -302,6 +302,7 @@ plt.show()
   
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    • 
 
 
diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs051.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs051.html
index fe4dd40f5..cbbe511d6 100644
--- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs051.html
+++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs051.html
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
                '___sec37'),
               ('Now Bagging', 2, None, '___sec38'),
               ('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
-              ('Random forests', 2, None, '___sec40'),
-              ('Random Forest Algorithm', 2, None, '___sec41'),
+              ('Changing the Level of the Decision Tree', 2, None, '___sec40'),
+              ('Random forests', 2, None, '___sec41'),
+              ('Random Forest Algorithm', 2, None, '___sec42'),
               ('Random Forests Compared with other Methods on the Cancer Data',
                2,
                None,
-               '___sec42'),
+               '___sec43'),
               ('Compare  Bagging on Trees with Random Forests',
                2,
                None,
-               '___sec43'),
-              ('Bootstrap with Random Forests Instead of a Single Tree',
+               '___sec44'),
+              ('Bootstrap with Random Forests Instead of a Single Tree, own '
+               'Bagging',
                2,
                None,
-               '___sec44'),
-              ("Boosting, a Bird'e Eye", 2, None, '___sec45'),
+               '___sec45'),
+              ("Boosting, a Bird'e Eye", 2, None, '___sec46'),
               ('Adaptive boosting: AdaBoost, Basic Algorithm',
                2,
                None,
-               '___sec46'),
-              ('Basic Steps of AdaBoost', 2, None, '___sec47'),
-              ('Figure to Illustrate the Iterative Classification Process',
-               2,
-               None,
-               '___sec48'),
+               '___sec47'),
+              ('Basic Steps of AdaBoost', 2, None, '___sec48'),
               ('AdaBoost Examples', 2, None, '___sec49'),
               ('Gradient boosting: Basics', 2, None, '___sec50'),
               ('Gradient Boosting, algorithm', 2, None, '___sec51'),
               ('Gradient Boosting, Examples', 2, None, '___sec52'),
               ('Gradient Boots with Early Stopping', 2, None, '___sec53'),
               ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
-              ('Xgboost on the Cancer Data', 2, None, '___sec55')]}
+              ('Regression Case', 2, None, '___sec55'),
+              ('Xgboost on the Cancer Data', 2, None, '___sec56')]}
 end of tocinfo -->
 
 
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
       
  • Please, not the moons again! Voting and Bagging
    • 
       
  • Now Bagging
    • 
       
  • Making our own Bagging with Bootstrap
    • 
-      
  • Random forests
    • 
-      
  • Random Forest Algorithm
    • 
-      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
-      
  • Compare  Bagging on Trees with Random Forests
    • 
-      
  • Bootstrap with Random Forests Instead of a Single Tree
    • 
-      
  • Boosting, a Bird'e Eye
    • 
-      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
-      
  • Basic Steps of AdaBoost
    • 
-      
  • Figure to Illustrate the Iterative Classification Process
    • 
+      
  • Changing the Level of the Decision Tree
    • 
+      
  • Random forests
    • 
+      
  • Random Forest Algorithm
    • 
+      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
+      
  • Compare  Bagging on Trees with Random Forests
    • 
+      
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • 
+      
  • Boosting, a Bird'e Eye
    • 
+      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
+      
  • Basic Steps of AdaBoost
    • 
       
  • AdaBoost Examples
    • 
       
  • Gradient boosting: Basics
    • 
       
  • Gradient Boosting, algorithm
    • 
       
  • Gradient Boosting, Examples
    • 
       
  • Gradient Boots with Early Stopping
    • 
       
  • XGBoost: Extreme Gradient Boosting
    • 
-      
  • Xgboost on the Cancer Data
    • 
+      
  • Regression Case
    • 
+      
  • Xgboost on the Cancer Data
    • 
 
         
       
@@ -273,6 +273,7 @@ function was the least squares function.
   
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    • 
 
 
diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs052.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs052.html
index 0f3729124..5b430bec9 100644
--- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs052.html
+++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs052.html
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
                '___sec37'),
               ('Now Bagging', 2, None, '___sec38'),
               ('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
-              ('Random forests', 2, None, '___sec40'),
-              ('Random Forest Algorithm', 2, None, '___sec41'),
+              ('Changing the Level of the Decision Tree', 2, None, '___sec40'),
+              ('Random forests', 2, None, '___sec41'),
+              ('Random Forest Algorithm', 2, None, '___sec42'),
               ('Random Forests Compared with other Methods on the Cancer Data',
                2,
                None,
-               '___sec42'),
+               '___sec43'),
               ('Compare  Bagging on Trees with Random Forests',
                2,
                None,
-               '___sec43'),
-              ('Bootstrap with Random Forests Instead of a Single Tree',
+               '___sec44'),
+              ('Bootstrap with Random Forests Instead of a Single Tree, own '
+               'Bagging',
                2,
                None,
-               '___sec44'),
-              ("Boosting, a Bird'e Eye", 2, None, '___sec45'),
+               '___sec45'),
+              ("Boosting, a Bird'e Eye", 2, None, '___sec46'),
               ('Adaptive boosting: AdaBoost, Basic Algorithm',
                2,
                None,
-               '___sec46'),
-              ('Basic Steps of AdaBoost', 2, None, '___sec47'),
-              ('Figure to Illustrate the Iterative Classification Process',
-               2,
-               None,
-               '___sec48'),
+               '___sec47'),
+              ('Basic Steps of AdaBoost', 2, None, '___sec48'),
               ('AdaBoost Examples', 2, None, '___sec49'),
               ('Gradient boosting: Basics', 2, None, '___sec50'),
               ('Gradient Boosting, algorithm', 2, None, '___sec51'),
               ('Gradient Boosting, Examples', 2, None, '___sec52'),
               ('Gradient Boots with Early Stopping', 2, None, '___sec53'),
               ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
-              ('Xgboost on the Cancer Data', 2, None, '___sec55')]}
+              ('Regression Case', 2, None, '___sec55'),
+              ('Xgboost on the Cancer Data', 2, None, '___sec56')]}
 end of tocinfo -->
 
 
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
       
  • Please, not the moons again! Voting and Bagging
    • 
       
  • Now Bagging
    • 
       
  • Making our own Bagging with Bootstrap
    • 
-      
  • Random forests
    • 
-      
  • Random Forest Algorithm
    • 
-      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
-      
  • Compare  Bagging on Trees with Random Forests
    • 
-      
  • Bootstrap with Random Forests Instead of a Single Tree
    • 
-      
  • Boosting, a Bird'e Eye
    • 
-      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
-      
  • Basic Steps of AdaBoost
    • 
-      
  • Figure to Illustrate the Iterative Classification Process
    • 
+      
  • Changing the Level of the Decision Tree
    • 
+      
  • Random forests
    • 
+      
  • Random Forest Algorithm
    • 
+      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
+      
  • Compare  Bagging on Trees with Random Forests
    • 
+      
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • 
+      
  • Boosting, a Bird'e Eye
    • 
+      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
+      
  • Basic Steps of AdaBoost
    • 
       
  • AdaBoost Examples
    • 
       
  • Gradient boosting: Basics
    • 
       
  • Gradient Boosting, algorithm
    • 
       
  • Gradient Boosting, Examples
    • 
       
  • Gradient Boots with Early Stopping
    • 
       
  • XGBoost: Extreme Gradient Boosting
    • 
-      
  • Xgboost on the Cancer Data
    • 
+      
  • Regression Case
    • 
+      
  • Xgboost on the Cancer Data
    • 
 
         
       
@@ -283,6 +283,7 @@ The way we proceed in an iterative fashion is to
   
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    • 
   
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    • 
   
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    • 
 
 
diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs053.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs053.html
index 591f2cd60..6dabddbf2 100644
--- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs053.html
+++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs053.html
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
                '___sec37'),
               ('Now Bagging', 2, None, '___sec38'),
               ('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
-              ('Random forests', 2, None, '___sec40'),
-              ('Random Forest Algorithm', 2, None, '___sec41'),
+              ('Changing the Level of the Decision Tree', 2, None, '___sec40'),
+              ('Random forests', 2, None, '___sec41'),
+              ('Random Forest Algorithm', 2, None, '___sec42'),
               ('Random Forests Compared with other Methods on the Cancer Data',
                2,
                None,
-               '___sec42'),
+               '___sec43'),
               ('Compare  Bagging on Trees with Random Forests',
                2,
                None,
-               '___sec43'),
-              ('Bootstrap with Random Forests Instead of a Single Tree',
+               '___sec44'),
+              ('Bootstrap with Random Forests Instead of a Single Tree, own '
+               'Bagging',
                2,
                None,
-               '___sec44'),
-              ("Boosting, a Bird'e Eye", 2, None, '___sec45'),
+               '___sec45'),
+              ("Boosting, a Bird'e Eye", 2, None, '___sec46'),
               ('Adaptive boosting: AdaBoost, Basic Algorithm',
                2,
                None,
-               '___sec46'),
-              ('Basic Steps of AdaBoost', 2, None, '___sec47'),
-              ('Figure to Illustrate the Iterative Classification Process',
-               2,
-               None,
-               '___sec48'),
+               '___sec47'),
+              ('Basic Steps of AdaBoost', 2, None, '___sec48'),
               ('AdaBoost Examples', 2, None, '___sec49'),
               ('Gradient boosting: Basics', 2, None, '___sec50'),
               ('Gradient Boosting, algorithm', 2, None, '___sec51'),
               ('Gradient Boosting, Examples', 2, None, '___sec52'),
               ('Gradient Boots with Early Stopping', 2, None, '___sec53'),
               ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
-              ('Xgboost on the Cancer Data', 2, None, '___sec55')]}
+              ('Regression Case', 2, None, '___sec55'),
+              ('Xgboost on the Cancer Data', 2, None, '___sec56')]}
 end of tocinfo -->
 
 
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
       
  • Please, not the moons again! Voting and Bagging
    • 
       
  • Now Bagging
    • 
       
  • Making our own Bagging with Bootstrap
    • 
-      
  • Random forests
    • 
-      
  • Random Forest Algorithm
    • 
-      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
-      
  • Compare  Bagging on Trees with Random Forests
    • 
-      
  • Bootstrap with Random Forests Instead of a Single Tree
    • 
-      
  • Boosting, a Bird'e Eye
    • 
-      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
-      
  • Basic Steps of AdaBoost
    • 
-      
  • Figure to Illustrate the Iterative Classification Process
    • 
+      
  • Changing the Level of the Decision Tree
    • 
+      
  • Random forests
    • 
+      
  • Random Forest Algorithm
    • 
+      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
+      
  • Compare  Bagging on Trees with Random Forests
    • 
+      
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • 
+      
  • Boosting, a Bird'e Eye
    • 
+      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
+      
  • Basic Steps of AdaBoost
    • 
       
  • AdaBoost Examples
    • 
       
  • Gradient boosting: Basics
    • 
       
  • Gradient Boosting, algorithm
    • 
       
  • Gradient Boosting, Examples
    • 
       
  • Gradient Boots with Early Stopping
    • 
       
  • XGBoost: Extreme Gradient Boosting
    • 
-      
  • Xgboost on the Cancer Data
    • 
+      
  • Regression Case
    • 
+      
  • Xgboost on the Cancer Data
    • 
 
         
       
@@ -346,6 +346,7 @@ plt.show()
   
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    • 
   
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diff --git a/doc/pub/DecisionTrees/html/DecisionTrees-bs.html b/doc/pub/DecisionTrees/html/DecisionTrees-bs.html
index a15bd51fb..cfc8c9a63 100644
--- a/doc/pub/DecisionTrees/html/DecisionTrees-bs.html
+++ b/doc/pub/DecisionTrees/html/DecisionTrees-bs.html
@@ -101,37 +101,36 @@ Automatically generated HTML file from DocOnce source
                '___sec37'),
               ('Now Bagging', 2, None, '___sec38'),
               ('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
-              ('Random forests', 2, None, '___sec40'),
-              ('Random Forest Algorithm', 2, None, '___sec41'),
+              ('Changing the Level of the Decision Tree', 2, None, '___sec40'),
+              ('Random forests', 2, None, '___sec41'),
+              ('Random Forest Algorithm', 2, None, '___sec42'),
               ('Random Forests Compared with other Methods on the Cancer Data',
                2,
                None,
-               '___sec42'),
+               '___sec43'),
               ('Compare  Bagging on Trees with Random Forests',
                2,
                None,
-               '___sec43'),
-              ('Bootstrap with Random Forests Instead of a Single Tree',
+               '___sec44'),
+              ('Bootstrap with Random Forests Instead of a Single Tree, own '
+               'Bagging',
                2,
                None,
-               '___sec44'),
-              ("Boosting, a Bird'e Eye", 2, None, '___sec45'),
+               '___sec45'),
+              ("Boosting, a Bird'e Eye", 2, None, '___sec46'),
               ('Adaptive boosting: AdaBoost, Basic Algorithm',
                2,
                None,
-               '___sec46'),
-              ('Basic Steps of AdaBoost', 2, None, '___sec47'),
-              ('Figure to Illustrate the Iterative Classification Process',
-               2,
-               None,
-               '___sec48'),
+               '___sec47'),
+              ('Basic Steps of AdaBoost', 2, None, '___sec48'),
               ('AdaBoost Examples', 2, None, '___sec49'),
               ('Gradient boosting: Basics', 2, None, '___sec50'),
               ('Gradient Boosting, algorithm', 2, None, '___sec51'),
               ('Gradient Boosting, Examples', 2, None, '___sec52'),
               ('Gradient Boots with Early Stopping', 2, None, '___sec53'),
               ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
-              ('Xgboost on the Cancer Data', 2, None, '___sec55')]}
+              ('Regression Case', 2, None, '___sec55'),
+              ('Xgboost on the Cancer Data', 2, None, '___sec56')]}
 end of tocinfo -->
 
 
@@ -209,22 +208,23 @@ MathJax.Hub.Config({
       
  • Please, not the moons again! Voting and Bagging
    • 
       
  • Now Bagging
    • 
       
  • Making our own Bagging with Bootstrap
    • 
-      
  • Random forests
    • 
-      
  • Random Forest Algorithm
    • 
-      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
-      
  • Compare  Bagging on Trees with Random Forests
    • 
-      
  • Bootstrap with Random Forests Instead of a Single Tree
    • 
-      
  • Boosting, a Bird'e Eye
    • 
-      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
-      
  • Basic Steps of AdaBoost
    • 
-      
  • Figure to Illustrate the Iterative Classification Process
    • 
+      
  • Changing the Level of the Decision Tree
    • 
+      
  • Random forests
    • 
+      
  • Random Forest Algorithm
    • 
+      
  • Random Forests Compared with other Methods on the Cancer Data
    • 
+      
  • Compare  Bagging on Trees with Random Forests
    • 
+      
  • Bootstrap with Random Forests Instead of a Single Tree, own Bagging
    • 
+      
  • Boosting, a Bird'e Eye
    • 
+      
  • Adaptive boosting: AdaBoost, Basic Algorithm
    • 
+      
  • Basic Steps of AdaBoost
    • 
       
  • AdaBoost Examples
    • 
       
  • Gradient boosting: Basics
    • 
       
  • Gradient Boosting, algorithm
    • 
       
  • Gradient Boosting, Examples
    • 
       
  • Gradient Boots with Early Stopping
    • 
       
  • XGBoost: Extreme Gradient Boosting
    • 
-      
  • Xgboost on the Cancer Data
    • 
+      
  • Regression Case
    • 
+      
  • Xgboost on the Cancer Data
    • 
 
         
       
@@ -283,7 +283,7 @@ MathJax.Hub.Config({
   
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    • 
   
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    • 
   
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    • 
-  
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diff --git a/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html b/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html
index 496aefa10..8a42b613e 100644
--- a/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html
+++ b/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html
@@ -1777,7 +1777,67 @@ plt.show()
 
 
 
    
-
    Random forests 
    
+
    Changing the Level of the Decision Tree 
    
+
+
    
+
+
+
    import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.utils import resample
+from sklearn.tree import DecisionTreeRegressor
+
+n = 100
+n_boostraps = 100
+maxdepth = 8
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+error = np.zeros(maxdepth)
+bias = np.zeros(maxdepth)
+variance = np.zeros(maxdepth)
+polydegree = np.zeros(maxdepth)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(1,maxdepth):
+    model = DecisionTreeRegressor(max_depth=degree) 
+    y_pred = np.empty((y_test.shape[0], n_boostraps))
+    for i in range(n_boostraps):
+        x_, y_ = resample(X_train_scaled, y_train)
+        model.fit(x_, y_)
+        y_pred[:, i] = model.predict(X_test_scaled)#.ravel()
+
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
+    variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
+    print('Polynomial degree:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.xlim(1,maxdepth)
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
    
+
    
+
+
+
    
+
    Random forests 
    
 
 
    
 Random forests provide an improvement over bagged trees by way of a
@@ -1823,7 +1883,7 @@ this setting.
 
 
 
    
-
    Random Forest Algorithm 
    
+
    Random Forest Algorithm 
    
 The algorithm described here can be applied to both classification and regression problems.
 
 
    
@@ -1854,7 +1914,7 @@ We will grow of forest of say \( M \) trees.
 
 
 
    
-
    Random Forests Compared with other Methods on the Cancer Data 
    
+
    Random Forests Compared with other Methods on the Cancer Data 
    
 
    
 
 
@@ -1928,7 +1988,7 @@ plt.show()
 
 
 
    
-
    Compare  Bagging on Trees with Random Forests  
    
+
    Compare  Bagging on Trees with Random Forests  
    
 
    
 
 
@@ -1951,7 +2011,7 @@ np.sum(y_pred == y_pred_rf) / len(y_pred)
 
 
 
    
-
    Bootstrap with Random Forests Instead of a Single Tree 
    
+
    Bootstrap with Random Forests Instead of a Single Tree, own Bagging 
    
 
 
    
 
@@ -1965,7 +2025,7 @@ np.sum(y_pred == y_pred_rf) / len(y_pred)
 
 np.random.seed(2018)
 
-n = 40
+n = 100
 n_boostraps = 100
 maxdegree = 14
 
@@ -1989,7 +2049,7 @@ X_test_scaled = scaler.transform(X_test)
     y_pred = np.empty((y_test.shape[0], n_boostraps))
     for i in range(n_boostraps):
         x_, y_ = resample(X_train_scaled, y_train)
-        model.fit(x_, y_)
+        model.fit(x_, y_.ravel())
         y_pred[:, i] = model.predict(X_test_scaled).ravel()
 
     polydegree[degree] = degree
@@ -2012,7 +2072,7 @@ plt.show()
 
 
 
    
-
    Boosting, a Bird'e Eye 
    
+
    Boosting, a Bird'e Eye 
    
 
 
    
 The basic idea is to combine weak classifiers in order to create a good
@@ -2029,7 +2089,7 @@ them with a factor.
 
 
 
    
-
    Adaptive boosting: AdaBoost, Basic Algorithm 
    
+
    Adaptive boosting: AdaBoost, Basic Algorithm 
    
 
 
    
 The algorithm here is rather straightforward. Assume that our weak
@@ -2053,7 +2113,7 @@ where the function \( I() \) is one if we misclassify and zero if we classify co
 
 
 
    
-
    Basic Steps of AdaBoost 
    
+
    Basic Steps of AdaBoost 
    
 
 
    
 With the above definitions we are now ready to set up the algorithm for AdaBoost.
@@ -2074,7 +2134,7 @@ $$
 
  •  Then we start looping over all attempts at classifying, namely we start an iterative process for \( m=1:M \), where \( M \) is the final number of classifications. Our given classifier could for example be a plain decision tree.
 
 
      
- 
    1.  Fit thus a given classifier to the training using the weights \( w_i \).
      2. 
+ 
    2.  Fit then a given classifier to the training using the weights \( w_i \).
      3. 
  
    3.  Compute then \( \mathrm{err} \) and figure out which events are classified properly and which are classified wrongly.
      4. 
  
    4.  Define a quantity $\alpha_{m} = \log{(1-\mathrm{err})/\mathrm{err}}
      5. 
  
    5.  Set the new weights to $w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*})}.
      6. 
@@ -2093,11 +2153,6 @@ observations that are missed in the previous iterations.
 
    • 
 
 
-
    
-
    Figure to Illustrate the Iterative Classification Process 
    
-
    
-
-
 
    
 
    AdaBoost Examples 
    
 
@@ -2363,7 +2418,63 @@ It is now the algorithm which wins essentially all ML competitions!!!
 
 
 
    
-
    Xgboost on the Cancer Data 
    
+
    Regression Case 
    
+
+
    
+
+
+
    import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+import xgboost as xgb
+from sklearn.preprocessing import StandardScaler
+import scikitplot as skplt
+from sklearn.metrics import mean_squared_error
+
+n = 40
+n_boostraps = 100
+maxdegree = 8
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+
+error = np.zeros(maxdegree)
+bias = np.zeros(maxdegree)
+variance = np.zeros(maxdegree)
+polydegree = np.zeros(maxdegree)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(maxdegree):
+    model =  xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
+                max_depth = maxdegree, alpha = 10, n_estimators = 10)
+    model.fit(X_train_scaled,y_train)
+    y_pred = model.predict(X_test_scaled)
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
+    variance[degree] = np.mean( np.var(y_pred) )
+    print('Max depth:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
    
+
    
+
+
+
    
+
    Xgboost on the Cancer Data 
    
 
    
 
 
diff --git a/doc/pub/DecisionTrees/html/DecisionTrees-solarized.html b/doc/pub/DecisionTrees/html/DecisionTrees-solarized.html
index b2c1e8ea1..e8e3b4ce1 100644
--- a/doc/pub/DecisionTrees/html/DecisionTrees-solarized.html
+++ b/doc/pub/DecisionTrees/html/DecisionTrees-solarized.html
@@ -121,37 +121,36 @@ div { text-align: justify; text-justify: inter-word; }
                '___sec37'),
               ('Now Bagging', 2, None, '___sec38'),
               ('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
-              ('Random forests', 2, None, '___sec40'),
-              ('Random Forest Algorithm', 2, None, '___sec41'),
+              ('Changing the Level of the Decision Tree', 2, None, '___sec40'),
+              ('Random forests', 2, None, '___sec41'),
+              ('Random Forest Algorithm', 2, None, '___sec42'),
               ('Random Forests Compared with other Methods on the Cancer Data',
                2,
                None,
-               '___sec42'),
+               '___sec43'),
               ('Compare  Bagging on Trees with Random Forests',
                2,
                None,
-               '___sec43'),
-              ('Bootstrap with Random Forests Instead of a Single Tree',
+               '___sec44'),
+              ('Bootstrap with Random Forests Instead of a Single Tree, own '
+               'Bagging',
                2,
                None,
-               '___sec44'),
-              ("Boosting, a Bird'e Eye", 2, None, '___sec45'),
+               '___sec45'),
+              ("Boosting, a Bird'e Eye", 2, None, '___sec46'),
               ('Adaptive boosting: AdaBoost, Basic Algorithm',
                2,
                None,
-               '___sec46'),
-              ('Basic Steps of AdaBoost', 2, None, '___sec47'),
-              ('Figure to Illustrate the Iterative Classification Process',
-               2,
-               None,
-               '___sec48'),
+               '___sec47'),
+              ('Basic Steps of AdaBoost', 2, None, '___sec48'),
               ('AdaBoost Examples', 2, None, '___sec49'),
               ('Gradient boosting: Basics', 2, None, '___sec50'),
               ('Gradient Boosting, algorithm', 2, None, '___sec51'),
               ('Gradient Boosting, Examples', 2, None, '___sec52'),
               ('Gradient Boots with Early Stopping', 2, None, '___sec53'),
               ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
-              ('Xgboost on the Cancer Data', 2, None, '___sec55')]}
+              ('Regression Case', 2, None, '___sec55'),
+              ('Xgboost on the Cancer Data', 2, None, '___sec56')]}
 end of tocinfo -->
 
 
@@ -1776,7 +1775,66 @@ plt.show()
 
    
 









    
 
-
    Random forests 
    
+
    Changing the Level of the Decision Tree 
    
+
+
    
+
+
+
    import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.utils import resample
+from sklearn.tree import DecisionTreeRegressor
+
+n = 100
+n_boostraps = 100
+maxdepth = 8
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+error = np.zeros(maxdepth)
+bias = np.zeros(maxdepth)
+variance = np.zeros(maxdepth)
+polydegree = np.zeros(maxdepth)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(1,maxdepth):
+    model = DecisionTreeRegressor(max_depth=degree) 
+    y_pred = np.empty((y_test.shape[0], n_boostraps))
+    for i in range(n_boostraps):
+        x_, y_ = resample(X_train_scaled, y_train)
+        model.fit(x_, y_)
+        y_pred[:, i] = model.predict(X_test_scaled)#.ravel()
+
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
+    variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
+    print('Polynomial degree:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.xlim(1,maxdepth)
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
    
+
    
+









    
+
+
    Random forests 
    
 
 
    
 Random forests provide an improvement over bagged trees by way of a
@@ -1820,7 +1878,7 @@ this setting.
 
    
 









    
 
-
    Random Forest Algorithm 
    
+
    Random Forest Algorithm 
    
 The algorithm described here can be applied to both classification and regression problems.
 
 
    
@@ -1846,7 +1904,7 @@ We will grow of forest of say \( M \) trees.
 
 









    
 
-
    Random Forests Compared with other Methods on the Cancer Data 
    
+
    Random Forests Compared with other Methods on the Cancer Data 
    
 
    
 
 
@@ -1919,7 +1977,7 @@ plt.show()
 
    
 









    
 
-
    Compare  Bagging on Trees with Random Forests  
    
+
    Compare  Bagging on Trees with Random Forests  
    
 
    
 
 
@@ -1941,7 +1999,7 @@ np.sum(y_pred == y_pred_rf) / len(y_pred)
 
    
 









    
 
-
    Bootstrap with Random Forests Instead of a Single Tree 
    
+
    Bootstrap with Random Forests Instead of a Single Tree, own Bagging 
    
 
 
    
 
@@ -1955,7 +2013,7 @@ np.sum(y_pred == y_pred_rf) / len(y_pred)
 
 np.random.seed(2018)
 
-n = 40
+n = 100
 n_boostraps = 100
 maxdegree = 14
 
@@ -1979,7 +2037,7 @@ X_test_scaled = scaler.transform(X_test)
     y_pred = np.empty((y_test.shape[0], n_boostraps))
     for i in range(n_boostraps):
         x_, y_ = resample(X_train_scaled, y_train)
-        model.fit(x_, y_)
+        model.fit(x_, y_.ravel())
         y_pred[:, i] = model.predict(X_test_scaled).ravel()
 
     polydegree[degree] = degree
@@ -2001,7 +2059,7 @@ plt.show()
 
    
 









    
 
-
    Boosting, a Bird'e Eye 
    
+
    Boosting, a Bird'e Eye 
    
 
 
    
 The basic idea is to combine weak classifiers in order to create a good
@@ -2018,7 +2076,7 @@ them with a factor.
 
    
 









    
 
-
    Adaptive boosting: AdaBoost, Basic Algorithm 
    
+
    Adaptive boosting: AdaBoost, Basic Algorithm 
    
 
 
    
 The algorithm here is rather straightforward. Assume that our weak
@@ -2040,7 +2098,7 @@ where the function \( I() \) is one if we misclassify and zero if we classify co
 
    
 









    
 
-
    Basic Steps of AdaBoost 
    
+
    Basic Steps of AdaBoost 
    
 
 
    
 With the above definitions we are now ready to set up the algorithm for AdaBoost.
@@ -2060,7 +2118,7 @@ $$
 
  •  Then we start looping over all attempts at classifying, namely we start an iterative process for \( m=1:M \), where \( M \) is the final number of classifications. Our given classifier could for example be a plain decision tree.
 
 
      
- 
    1.  Fit thus a given classifier to the training using the weights \( w_i \).
      2. 
+ 
    2.  Fit then a given classifier to the training using the weights \( w_i \).
      3. 
  
    3.  Compute then \( \mathrm{err} \) and figure out which events are classified properly and which are classified wrongly.
      4. 
  
    4.  Define a quantity $\alpha_{m} = \log{(1-\mathrm{err})/\mathrm{err}}
      5. 
  
    5.  Set the new weights to $w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*})}.
      6. 
@@ -2080,11 +2138,6 @@ observations that are missed in the previous iterations.
 
      
 









      
 
-
      Figure to Illustrate the Iterative Classification Process 
      
-
-
      
-









      
-
 
      AdaBoost Examples 
      
 
 
      
@@ -2344,7 +2397,62 @@ It is now the algorithm which wins essentially all ML competitions!!!
 
      
 









      
 
-
      Xgboost on the Cancer Data 
      
+
      Regression Case 
      
+
+
      
+
+
+
      import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+import xgboost as xgb
+from sklearn.preprocessing import StandardScaler
+import scikitplot as skplt
+from sklearn.metrics import mean_squared_error
+
+n = 40
+n_boostraps = 100
+maxdegree = 8
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+
+error = np.zeros(maxdegree)
+bias = np.zeros(maxdegree)
+variance = np.zeros(maxdegree)
+polydegree = np.zeros(maxdegree)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(maxdegree):
+    model =  xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
+                max_depth = maxdegree, alpha = 10, n_estimators = 10)
+    model.fit(X_train_scaled,y_train)
+    y_pred = model.predict(X_test_scaled)
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
+    variance[degree] = np.mean( np.var(y_pred) )
+    print('Max depth:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
      
+
      
+









      
+
+
      Xgboost on the Cancer Data 
      
 
      
 
 
diff --git a/doc/pub/DecisionTrees/html/DecisionTrees.html b/doc/pub/DecisionTrees/html/DecisionTrees.html
index ede4f121d..180cfd4e8 100644
--- a/doc/pub/DecisionTrees/html/DecisionTrees.html
+++ b/doc/pub/DecisionTrees/html/DecisionTrees.html
@@ -126,37 +126,36 @@ div { text-align: justify; text-justify: inter-word; }
                '___sec37'),
               ('Now Bagging', 2, None, '___sec38'),
               ('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
-              ('Random forests', 2, None, '___sec40'),
-              ('Random Forest Algorithm', 2, None, '___sec41'),
+              ('Changing the Level of the Decision Tree', 2, None, '___sec40'),
+              ('Random forests', 2, None, '___sec41'),
+              ('Random Forest Algorithm', 2, None, '___sec42'),
               ('Random Forests Compared with other Methods on the Cancer Data',
                2,
                None,
-               '___sec42'),
+               '___sec43'),
               ('Compare  Bagging on Trees with Random Forests',
                2,
                None,
-               '___sec43'),
-              ('Bootstrap with Random Forests Instead of a Single Tree',
+               '___sec44'),
+              ('Bootstrap with Random Forests Instead of a Single Tree, own '
+               'Bagging',
                2,
                None,
-               '___sec44'),
-              ("Boosting, a Bird'e Eye", 2, None, '___sec45'),
+               '___sec45'),
+              ("Boosting, a Bird'e Eye", 2, None, '___sec46'),
               ('Adaptive boosting: AdaBoost, Basic Algorithm',
                2,
                None,
-               '___sec46'),
-              ('Basic Steps of AdaBoost', 2, None, '___sec47'),
-              ('Figure to Illustrate the Iterative Classification Process',
-               2,
-               None,
-               '___sec48'),
+               '___sec47'),
+              ('Basic Steps of AdaBoost', 2, None, '___sec48'),
               ('AdaBoost Examples', 2, None, '___sec49'),
               ('Gradient boosting: Basics', 2, None, '___sec50'),
               ('Gradient Boosting, algorithm', 2, None, '___sec51'),
               ('Gradient Boosting, Examples', 2, None, '___sec52'),
               ('Gradient Boots with Early Stopping', 2, None, '___sec53'),
               ('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
-              ('Xgboost on the Cancer Data', 2, None, '___sec55')]}
+              ('Regression Case', 2, None, '___sec55'),
+              ('Xgboost on the Cancer Data', 2, None, '___sec56')]}
 end of tocinfo -->
 
 
@@ -1781,7 +1780,66 @@ plt.show()
 
      
 









      
 
-
      Random forests 
      
+
      Changing the Level of the Decision Tree 
      
+
+
      
+
+
+
      import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.utils import resample
+from sklearn.tree import DecisionTreeRegressor
+
+n = 100
+n_boostraps = 100
+maxdepth = 8
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+error = np.zeros(maxdepth)
+bias = np.zeros(maxdepth)
+variance = np.zeros(maxdepth)
+polydegree = np.zeros(maxdepth)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(1,maxdepth):
+    model = DecisionTreeRegressor(max_depth=degree) 
+    y_pred = np.empty((y_test.shape[0], n_boostraps))
+    for i in range(n_boostraps):
+        x_, y_ = resample(X_train_scaled, y_train)
+        model.fit(x_, y_)
+        y_pred[:, i] = model.predict(X_test_scaled)#.ravel()
+
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
+    variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
+    print('Polynomial degree:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.xlim(1,maxdepth)
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
      
+
      
+









      
+
+
      Random forests 
      
 
 
      
 Random forests provide an improvement over bagged trees by way of a
@@ -1825,7 +1883,7 @@ this setting.
 
      
 









      
 
-
      Random Forest Algorithm 
      
+
      Random Forest Algorithm 
      
 The algorithm described here can be applied to both classification and regression problems.
 
 
      
@@ -1851,7 +1909,7 @@ We will grow of forest of say \( M \) trees.
 
 









      
 
-
      Random Forests Compared with other Methods on the Cancer Data 
      
+
      Random Forests Compared with other Methods on the Cancer Data 
      
 
      
 
 
@@ -1924,7 +1982,7 @@ plt.show()
 
      
 









      
 
-
      Compare  Bagging on Trees with Random Forests  
      
+
      Compare  Bagging on Trees with Random Forests  
      
 
      
 
 
@@ -1946,7 +2004,7 @@ np.sum(y_pred =
 
      
 









      
 
-
      Bootstrap with Random Forests Instead of a Single Tree 
      
+
      Bootstrap with Random Forests Instead of a Single Tree, own Bagging 
      
 
 
      
 
@@ -1960,7 +2018,7 @@ np.sum(y_pred =
 
 np.random.seed(2018)
 
-n = 40
+n = 100
 n_boostraps = 100
 maxdegree = 14
 
@@ -1984,7 +2042,7 @@ X_test_scaled = scaler= np.empty((y_test.shape[0], n_boostraps))
     for i in range(n_boostraps):
         x_, y_ = resample(X_train_scaled, y_train)
-        model.fit(x_, y_)
+        model.fit(x_, y_.ravel())
         y_pred[:, i] = model.predict(X_test_scaled).ravel()
 
     polydegree[degree] = degree
@@ -2006,7 +2064,7 @@ plt.show()
 
      
 









      
 
-
      Boosting, a Bird'e Eye 
      
+
      Boosting, a Bird'e Eye 
      
 
 
      
 The basic idea is to combine weak classifiers in order to create a good
@@ -2023,7 +2081,7 @@ them with a factor.
 
      
 









      
 
-
      Adaptive boosting: AdaBoost, Basic Algorithm 
      
+
      Adaptive boosting: AdaBoost, Basic Algorithm 
      
 
 
      
 The algorithm here is rather straightforward. Assume that our weak
@@ -2045,7 +2103,7 @@ where the function \( I() \) is one if we misclassify and zero if we classify co
 
      
 









      
 
-
      Basic Steps of AdaBoost 
      
+
      Basic Steps of AdaBoost 
      
 
 
      
 With the above definitions we are now ready to set up the algorithm for AdaBoost.
@@ -2065,7 +2123,7 @@ $$
 
    6.  Then we start looping over all attempts at classifying, namely we start an iterative process for \( m=1:M \), where \( M \) is the final number of classifications. Our given classifier could for example be a plain decision tree.
 
 
        
- 
      1.  Fit thus a given classifier to the training using the weights \( w_i \).
        2. 
+ 
      2.  Fit then a given classifier to the training using the weights \( w_i \).
        3. 
  
      3.  Compute then \( \mathrm{err} \) and figure out which events are classified properly and which are classified wrongly.
        4. 
  
      4.  Define a quantity $\alpha_{m} = \log{(1-\mathrm{err})/\mathrm{err}}
        5. 
  
      5.  Set the new weights to $w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(\boldsymbol{X}_{i*})}.
        6. 
@@ -2085,11 +2143,6 @@ observations that are missed in the previous iterations.
 
        
 









        
 
-
        Figure to Illustrate the Iterative Classification Process 
        
-
-
        
-









        
-
 
        AdaBoost Examples 
        
 
 
        
@@ -2349,7 +2402,62 @@ It is now the algorithm which wins essentially all ML competitions!!!
 
        
 









        
 
-
        Xgboost on the Cancer Data 
        
+
        Regression Case 
        
+
+
        
+
+
+
        import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+import xgboost as xgb
+from sklearn.preprocessing import StandardScaler
+import scikitplot as skplt
+from sklearn.metrics import mean_squared_error
+
+n = 40
+n_boostraps = 100
+maxdegree = 8
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+
+error = np.zeros(maxdegree)
+bias = np.zeros(maxdegree)
+variance = np.zeros(maxdegree)
+polydegree = np.zeros(maxdegree)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(maxdegree):
+    model =  xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
+                max_depth = maxdegree, alpha = 10, n_estimators = 10)
+    model.fit(X_train_scaled,y_train)
+    y_pred = model.predict(X_test_scaled)
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
+    variance[degree] = np.mean( np.var(y_pred) )
+    print('Max depth:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
        
+
        
+









        
+
+
        Xgboost on the Cancer Data 
        
 
        
 
 
diff --git a/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb b/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb
index 59ba61dcb..c27d3d1be 100644
--- a/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb
+++ b/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb
@@ -1796,6 +1796,74 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Changing the Level of the Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.utils import resample\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "\n",
+    "n = 100\n",
+    "n_boostraps = 100\n",
+    "maxdepth = 8\n",
+    "\n",
+    "# Make data set.\n",
+    "x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
+    "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n",
+    "error = np.zeros(maxdepth)\n",
+    "bias = np.zeros(maxdepth)\n",
+    "variance = np.zeros(maxdepth)\n",
+    "polydegree = np.zeros(maxdepth)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
+    "\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_train)\n",
+    "X_train_scaled = scaler.transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "for degree in range(1,maxdepth):\n",
+    "    model = DecisionTreeRegressor(max_depth=degree) \n",
+    "    y_pred = np.empty((y_test.shape[0], n_boostraps))\n",
+    "    for i in range(n_boostraps):\n",
+    "        x_, y_ = resample(X_train_scaled, y_train)\n",
+    "        model.fit(x_, y_)\n",
+    "        y_pred[:, i] = model.predict(X_test_scaled)#.ravel()\n",
+    "\n",
+    "    polydegree[degree] = degree\n",
+    "    error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )\n",
+    "    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )\n",
+    "    variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )\n",
+    "    print('Polynomial degree:', degree)\n",
+    "    print('Error:', error[degree])\n",
+    "    print('Bias^2:', bias[degree])\n",
+    "    print('Var:', variance[degree])\n",
+    "    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n",
+    "\n",
+    "plt.xlim(1,maxdepth)\n",
+    "plt.plot(polydegree, error, label='Error')\n",
+    "plt.plot(polydegree, bias, label='bias')\n",
+    "plt.plot(polydegree, variance, label='Variance')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1872,7 +1940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -1954,7 +2022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -1967,7 +2035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -1986,12 +2054,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Bootstrap with Random Forests Instead of a Single Tree"
+    "## Bootstrap with Random Forests Instead of a Single Tree, own Bagging"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
@@ -2007,7 +2075,7 @@
     "\n",
     "np.random.seed(2018)\n",
     "\n",
-    "n = 40\n",
+    "n = 100\n",
     "n_boostraps = 100\n",
     "maxdegree = 14\n",
     "\n",
@@ -2031,7 +2099,7 @@
     "    y_pred = np.empty((y_test.shape[0], n_boostraps))\n",
     "    for i in range(n_boostraps):\n",
     "        x_, y_ = resample(X_train_scaled, y_train)\n",
-    "        model.fit(x_, y_)\n",
+    "        model.fit(x_, y_.ravel())\n",
     "        y_pred[:, i] = model.predict(X_test_scaled).ravel()\n",
     "\n",
     "    polydegree[degree] = degree\n",
@@ -2120,7 +2188,7 @@
    "source": [
     "1. Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.\n",
     "\n",
-    "a. Fit thus a given classifier to the training using the weights $w_i$.\n",
+    "a. Fit then a given classifier to the training using the weights $w_i$.\n",
     "\n",
     "b. Compute then $\\mathrm{err}$ and figure out which events are classified properly and which are classified wrongly.\n",
     "\n",
@@ -2139,7 +2207,6 @@
     "new classification step $m$ is then forced to concentrate on those\n",
     "observations that are missed in the previous iterations.\n",
     "\n",
-    "## Figure to Illustrate the Iterative Classification Process\n",
     "\n",
     "\n",
     "## AdaBoost Examples\n",
@@ -2149,7 +2216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -2239,7 +2306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -2338,7 +2405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
@@ -2423,12 +2490,75 @@
     "\n",
     "It is now the algorithm which wins essentially all ML competitions!!!\n",
     "\n",
+    "## Regression Case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import xgboost as xgb\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import scikitplot as skplt\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "n = 40\n",
+    "n_boostraps = 100\n",
+    "maxdegree = 8\n",
+    "\n",
+    "# Make data set.\n",
+    "x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
+    "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n",
+    "\n",
+    "error = np.zeros(maxdegree)\n",
+    "bias = np.zeros(maxdegree)\n",
+    "variance = np.zeros(maxdegree)\n",
+    "polydegree = np.zeros(maxdegree)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_train)\n",
+    "X_train_scaled = scaler.transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "for degree in range(maxdegree):\n",
+    "    model =  xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,\n",
+    "                max_depth = maxdegree, alpha = 10, n_estimators = 10)\n",
+    "    model.fit(X_train_scaled,y_train)\n",
+    "    y_pred = model.predict(X_test_scaled)\n",
+    "    polydegree[degree] = degree\n",
+    "    error[degree] = np.mean( np.mean((y_test - y_pred)**2) )\n",
+    "    bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )\n",
+    "    variance[degree] = np.mean( np.var(y_pred) )\n",
+    "    print('Max depth:', degree)\n",
+    "    print('Error:', error[degree])\n",
+    "    print('Bias^2:', bias[degree])\n",
+    "    print('Var:', variance[degree])\n",
+    "    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n",
+    "\n",
+    "plt.plot(polydegree, error, label='Error')\n",
+    "plt.plot(polydegree, bias, label='bias')\n",
+    "plt.plot(polydegree, variance, label='Variance')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Xgboost on the Cancer Data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
diff --git a/doc/pub/DecisionTrees/ipynb/ipynb-DecisionTrees-src.tar.gz b/doc/pub/DecisionTrees/ipynb/ipynb-DecisionTrees-src.tar.gz
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diff --git a/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf b/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf
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zsl~Z0mUUZ=sXDjFCC{3!5$bmfMtG2D>NoOJ;49UmVrM>`Z7MmW+kE#s{32mn)LD&T
zx-5z*WVCn$`1aNmx8-#~wpNFRl1ML~{CU1)!l5lAwvbEH$RJ%&8}uuB?NFX^O0B}~
zOe#x>j2QG=&5r+xuKOD#z}t4bR2d~5`0)$O$MX}j9VzXU6;Q?Gl9;N{I+1
T`2rCJ2qe+~sG(u&YzO=cHT= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.xlim(1,maxdepth)
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
+
+
+
+!ec
+
+
+
 
 !split
 ===== Random forests =====
@@ -1599,7 +1662,7 @@ np.sum(y_pred == y_pred_rf) / len(y_pred)
 
 
 !split
-===== Bootstrap with Random Forests Instead of a Single Tree =====
+===== Bootstrap with Random Forests Instead of a Single Tree, own Bagging =====
 
 !bc pycod
 
@@ -1612,7 +1675,7 @@ from sklearn.ensemble import RandomForestRegressor
 
 np.random.seed(2018)
 
-n = 40
+n = 100
 n_boostraps = 100
 maxdegree = 14
 
@@ -1636,7 +1699,7 @@ for degree in range(maxdegree):
     y_pred = np.empty((y_test.shape[0], n_boostraps))
     for i in range(n_boostraps):
         x_, y_ = resample(X_train_scaled, y_train)
-        model.fit(x_, y_)
+        model.fit(x_, y_.ravel())
         y_pred[:, i] = model.predict(X_test_scaled).ravel()
 
     polydegree[degree] = degree
@@ -1707,7 +1770,7 @@ o We rewrite the misclassification error as
 \]
 !et
 o Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.
-  o Fit thus a given classifier to the training using the weights $w_i$.
+  o Fit then a given classifier to the training using the weights $w_i$.
   o Compute then $\mathrm{err}$ and figure out which events are classified properly and which are classified wrongly.
   o Define a quantity $\alpha_{m} = \log{(1-\mathrm{err})/\mathrm{err}}
   o Set the new weights to $w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(\bm{X}_{i*})}.
@@ -1721,8 +1784,6 @@ difficult to classifiy correctly are given a larger influence. Each
 new classification step $m$ is then forced to concentrate on those
 observations that are missed in the previous iterations.
 
-!split
-===== Figure to Illustrate the Iterative Classification Process =====
 
 
 !split
@@ -1965,6 +2026,62 @@ sketch for efficient proposal calculation. It introduces a novel sparsity-aware
 
 It is now the algorithm which wins essentially all ML competitions!!!
 
+!split
+===== Regression Case =====
+
+!bc pycod
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+import xgboost as xgb
+from sklearn.preprocessing import StandardScaler
+import scikitplot as skplt
+from sklearn.metrics import mean_squared_error
+
+n = 40
+n_boostraps = 100
+maxdegree = 8
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+
+error = np.zeros(maxdegree)
+bias = np.zeros(maxdegree)
+variance = np.zeros(maxdegree)
+polydegree = np.zeros(maxdegree)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(maxdegree):
+    model =  xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
+                max_depth = maxdegree, alpha = 10, n_estimators = 10)
+    model.fit(X_train_scaled,y_train)
+    y_pred = model.predict(X_test_scaled)
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
+    variance[degree] = np.mean( np.var(y_pred) )
+    print('Max depth:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
+
+!ec
+
+
+
 !split
 ===== Xgboost on the Cancer Data =====
 !bc pycod
diff --git a/doc/src/DecisionTrees/Programs/bootstrap.py~ b/doc/src/DecisionTrees/Programs/bootstrap.py~
new file mode 100644
index 000000000..623de3af2
--- /dev/null
+++ b/doc/src/DecisionTrees/Programs/bootstrap.py~
@@ -0,0 +1,56 @@
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.utils import resample
+from sklearn.tree import DecisionTreeRegressor
+
+
+np.random.seed(2018)
+
+n = 40
+n_boostraps = 100
+maxdegree = 14
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+error = np.zeros(maxdegree)
+bias = np.zeros(maxdegree)
+variance = np.zeros(maxdegree)
+polydegree = np.zeros(maxdegree)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(maxdegree):
+    model = DecisionTreeRegressor(max_depth=2) 
+    y_pred = np.empty((y_test.shape[0], n_boostraps))
+    for i in range(n_boostraps):
+        x_, y_ = resample(X_train_scaled, y_train)
+        model.fit(x_, y_)
+        y_pred[:, i] = model.predict(X_test_scaled).ravel()
+
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
+    variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
+    print('Polynomial degree:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
+
+
diff --git a/doc/src/DecisionTrees/Programs/rfbootclassify.py b/doc/src/DecisionTrees/Programs/rfbootclassify.py
new file mode 100644
index 000000000..6bd25a532
--- /dev/null
+++ b/doc/src/DecisionTrees/Programs/rfbootclassify.py
@@ -0,0 +1,54 @@
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.utils import resample
+from sklearn.tree import DecisionTreeRegressor
+
+n = 100
+n_boostraps = 100
+maxdepth = 8
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+error = np.zeros(maxdepth)
+bias = np.zeros(maxdepth)
+variance = np.zeros(maxdepth)
+polydegree = np.zeros(maxdepth)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(1,maxdepth):
+    model = DecisionTreeRegressor(max_depth=degree) 
+    y_pred = np.empty((y_test.shape[0], n_boostraps))
+    for i in range(n_boostraps):
+        x_, y_ = resample(X_train_scaled, y_train)
+        model.fit(x_, y_)
+        y_pred[:, i] = model.predict(X_test_scaled)#.ravel()
+
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
+    variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
+    print('Polynomial degree:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.xlim(1,maxdepth)
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
+
+
diff --git a/doc/src/DecisionTrees/Programs/rfbootclassify.py~ b/doc/src/DecisionTrees/Programs/rfbootclassify.py~
new file mode 100644
index 000000000..84994c400
--- /dev/null
+++ b/doc/src/DecisionTrees/Programs/rfbootclassify.py~
@@ -0,0 +1,54 @@
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.utils import resample
+from sklearn.tree import DecisionTreeRegressor
+
+n = 100
+n_boostraps = 100
+maxdepth = 8
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+error = np.zeros(maxdepth)
+bias = np.zeros(maxdepth)
+variance = np.zeros(maxdepth)
+polydegree = np.zeros(maxdepth)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(1,maxdepth):
+    model = DecisionTreeRegressor(max_depth=degree) 
+    y_pred = np.empty((y_test.shape[0], n_boostraps))
+    for i in range(n_boostraps):
+        x_, y_ = resample(X_train_scaled, y_train)
+        model.fit(x_, y_)
+        y_pred[:, i] = model.predict(X_test_scaled)#.ravel()
+
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
+    variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
+    print('Polynomial degree:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.xlim(1,maxdegree)
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
+
+
diff --git a/doc/src/DecisionTrees/Programs/rfbootstrap.py b/doc/src/DecisionTrees/Programs/rfbootstrap.py
index 824862286..db31345b5 100644
--- a/doc/src/DecisionTrees/Programs/rfbootstrap.py
+++ b/doc/src/DecisionTrees/Programs/rfbootstrap.py
@@ -8,13 +8,14 @@ from sklearn.ensemble import RandomForestRegressor
 
 np.random.seed(2018)
 
-n = 40
+n = 500
 n_boostraps = 100
 maxdegree = 14
 
 # Make data set.
 x = np.linspace(-3, 3, n).reshape(-1, 1)
 y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+
 error = np.zeros(maxdegree)
 bias = np.zeros(maxdegree)
 variance = np.zeros(maxdegree)
@@ -32,8 +33,8 @@ for degree in range(maxdegree):
     y_pred = np.empty((y_test.shape[0], n_boostraps))
     for i in range(n_boostraps):
         x_, y_ = resample(X_train_scaled, y_train)
-        model.fit(x_, y_)
-        y_pred[:, i] = model.predict(X_test_scaled)#.ravel()
+        model.fit(x_, y_.ravel())
+        y_pred[:, i] = model.predict(X_test_scaled)
 
     polydegree[degree] = degree
     error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
diff --git a/doc/src/DecisionTrees/Programs/rfbootstrap.py~ b/doc/src/DecisionTrees/Programs/rfbootstrap.py~
new file mode 100644
index 000000000..96f81e3df
--- /dev/null
+++ b/doc/src/DecisionTrees/Programs/rfbootstrap.py~
@@ -0,0 +1,54 @@
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.utils import resample
+from sklearn.ensemble import RandomForestRegressor
+
+np.random.seed(2018)
+
+n = 40
+n_boostraps = 100
+maxdegree = 14
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+error = np.zeros(maxdegree)
+bias = np.zeros(maxdegree)
+variance = np.zeros(maxdegree)
+polydegree = np.zeros(maxdegree)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(maxdegree):
+    model = RandomForestRegressor()
+    y_pred = np.empty((y_test.shape[0], n_boostraps))
+    for i in range(n_boostraps):
+        x_, y_ = resample(X_train_scaled, y_train).ravel()
+        model.fit(x_, y_)
+        y_pred[:, i] = model.predict(X_test_scaled).ravel()
+
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
+    variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
+    print('Polynomial degree:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
+
diff --git a/doc/src/DecisionTrees/Programs/rfcancer.py b/doc/src/DecisionTrees/Programs/rfcancer.py
index 9f226f064..d507268cb 100644
--- a/doc/src/DecisionTrees/Programs/rfcancer.py
+++ b/doc/src/DecisionTrees/Programs/rfcancer.py
@@ -29,8 +29,6 @@ accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_
 print(accuracy)
 print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(Random_Forest_model.score(X_test_scaled,y_test)))
 
-
-
 y_pred = Random_Forest_model.predict(X_test_scaled)
 skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
 plt.show()
diff --git a/doc/src/DecisionTrees/Programs/xgcancer.py~ b/doc/src/DecisionTrees/Programs/xgcancer.py~
new file mode 100644
index 000000000..9f226f064
--- /dev/null
+++ b/doc/src/DecisionTrees/Programs/xgcancer.py~
@@ -0,0 +1,41 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import  train_test_split 
+from sklearn.datasets import load_breast_cancer
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+import scikitplot as skplt
+
+# Load the data
+cancer = load_breast_cancer()
+
+X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
+print(X_train.shape)
+print(X_test.shape)
+#now scale the data
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+# Data set not specificied
+#Instantiate the model with 100 trees and entropy as splitting criteria
+Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion="entropy")
+Random_Forest_model.fit(X_train_scaled, y_train)
+#Cross validation
+accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']
+print(accuracy)
+print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(Random_Forest_model.score(X_test_scaled,y_test)))
+
+
+
+y_pred = Random_Forest_model.predict(X_test_scaled)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
+plt.show()
+y_probas = Random_Forest_model.predict_proba(X_test_scaled)
+skplt.metrics.plot_roc(y_test, y_probas)
+plt.show()
+skplt.metrics.plot_cumulative_gain(y_test, y_probas)
+plt.show()
diff --git a/doc/src/DecisionTrees/Programs/xgregressor.py b/doc/src/DecisionTrees/Programs/xgregressor.py
new file mode 100644
index 000000000..0aaf7be2f
--- /dev/null
+++ b/doc/src/DecisionTrees/Programs/xgregressor.py
@@ -0,0 +1,47 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+import xgboost as xgb
+from sklearn.preprocessing import StandardScaler
+import scikitplot as skplt
+from sklearn.metrics import mean_squared_error
+
+n = 40
+n_boostraps = 100
+maxdegree = 8
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+
+error = np.zeros(maxdegree)
+bias = np.zeros(maxdegree)
+variance = np.zeros(maxdegree)
+polydegree = np.zeros(maxdegree)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(maxdegree):
+    model =  xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
+                max_depth = maxdegree, alpha = 10, n_estimators = 10)
+    model.fit(X_train_scaled,y_train)
+    y_pred = model.predict(X_test_scaled)
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
+    variance[degree] = np.mean( np.var(y_pred) )
+    print('Polynomial degree:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
diff --git a/doc/src/DecisionTrees/Programs/xgregressor.py~ b/doc/src/DecisionTrees/Programs/xgregressor.py~
new file mode 100644
index 000000000..6c9b2a16d
--- /dev/null
+++ b/doc/src/DecisionTrees/Programs/xgregressor.py~
@@ -0,0 +1,47 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+import xgboost as xgb
+from sklearn.preprocessing import StandardScaler
+import scikitplot as skplt
+from sklearn.metrics import mean_squared_error
+
+n = 500
+n_boostraps = 100
+maxdegree = 8
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+
+error = np.zeros(maxdegree)
+bias = np.zeros(maxdegree)
+variance = np.zeros(maxdegree)
+polydegree = np.zeros(maxdegree)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(maxdegree):
+    model =  xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
+                max_depth = maxdegree, alpha = 10, n_estimators = 10)
+    model.fit(X_train_scaled,y_train)
+    y_pred = model.predict(X_test_scaled)
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
+    variance[degree] = np.mean( np.var(y_pred) )
+    print('Polynomial degree:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+