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added boost
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‎doc/pub/DecisionTrees/html/._DecisionTrees-bs000.html‎

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('Final regressor code', 2, None, '___sec28'),
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('Pros and cons of trees, pros', 2, None, '___sec29'),
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('Disadvantages', 2, None, '___sec30'),
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('From a Single Tree to Many Trees, Meet the Jungle of Methods',
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('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
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'Boosting, Meet the Jungle of Methods',
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'___sec31'),
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None,
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'___sec37'),
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('Now Bagging', 2, None, '___sec38'),
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('Random forests', 2, None, '___sec39'),
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('Random Forest Algorithm', 2, None, '___sec40'),
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('Making our own Bagging with Bootstrap', 2, None, '___sec39'),
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('Random forests', 2, None, '___sec40'),
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('Random Forest Algorithm', 2, None, '___sec41'),
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('Random Forests Compared with other Methods on the Cancer Data',
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2,
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None,
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'___sec41'),
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'___sec42'),
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('Compare Bagging on Trees with Random Forests',
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None,
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'___sec42'),
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('Feature Importance', 2, None, '___sec43'),
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("Boosting, a Bird'e Eye", 2, None, '___sec44'),
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'___sec43'),
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('Bootstrap with Random Forests Instead of a Single Tree',
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2,
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None,
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'___sec44'),
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("Boosting, a Bird'e Eye", 2, None, '___sec45'),
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('Adaptive boosting: AdaBoost, Basic Algorithm',
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None,
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'___sec45'),
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('Basic Steps of AdaBoost', 2, None, '___sec46'),
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'___sec46'),
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('Basic Steps of AdaBoost', 2, None, '___sec47'),
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('Figure to Illustrate the Iterative Classification Process',
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None,
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'___sec47'),
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('AdaBoost Examples', 2, None, '___sec48'),
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('Gradient boosting: Basics', 2, None, '___sec49'),
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('Gradient Boosting, algorithm', 2, None, '___sec50'),
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('Gradient Boosting, Examples', 2, None, '___sec51'),
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('Gradient Boots with Early Stopping', 2, None, '___sec52'),
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('XGBoost: Extreme Gradient Boosting', 2, None, '___sec53'),
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('Xgboost on the Cancer Data', 2, None, '___sec54')]}
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'___sec48'),
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('AdaBoost Examples', 2, None, '___sec49'),
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('Gradient boosting: Basics', 2, None, '___sec50'),
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('Gradient Boosting, algorithm', 2, None, '___sec51'),
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('Gradient Boosting, Examples', 2, None, '___sec52'),
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('Gradient Boots with Early Stopping', 2, None, '___sec53'),
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('XGBoost: Extreme Gradient Boosting', 2, None, '___sec54'),
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('Xgboost on the Cancer Data', 2, None, '___sec55')]}
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<body>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs029.html#___sec28" style="font-size: 80%;">Final regressor code</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs030.html#___sec29" style="font-size: 80%;">Pros and cons of trees, pros</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs031.html#___sec30" style="font-size: 80%;">Disadvantages</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs032.html#___sec31" style="font-size: 80%;">From a Single Tree to Many Trees, Meet the Jungle of Methods</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs032.html#___sec31" style="font-size: 80%;">Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs033.html#___sec32" style="font-size: 80%;">An Overview of Ensemble Methods</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs034.html#___sec33" style="font-size: 80%;">Bagging</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs035.html#___sec34" style="font-size: 80%;">More bagging</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs036.html#___sec35" style="font-size: 80%;">Simple Voting Example, head or tail</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs037.html#___sec36" style="font-size: 80%;">Using the Voting Classifier</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs039.html#___sec38" style="font-size: 80%;">Now Bagging</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Random forests</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random Forest Algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Feature Importance</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">AdaBoost Examples</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">Gradient boosting: Basics</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs040.html#___sec39" style="font-size: 80%;">Making our own Bagging with Bootstrap</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs041.html#___sec40" style="font-size: 80%;">Random forests</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs042.html#___sec41" style="font-size: 80%;">Random Forest Algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs043.html#___sec42" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs044.html#___sec43" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs045.html#___sec44" style="font-size: 80%;">Bootstrap with Random Forests Instead of a Single Tree</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs046.html#___sec45" style="font-size: 80%;">Boosting, a Bird'e Eye</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs047.html#___sec46" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs048.html#___sec47" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs049.html#___sec48" style="font-size: 80%;">Figure to Illustrate the Iterative Classification Process</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs050.html#___sec49" style="font-size: 80%;">AdaBoost Examples</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs051.html#___sec50" style="font-size: 80%;">Gradient boosting: Basics</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs052.html#___sec51" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs053.html#___sec52" style="font-size: 80%;">Gradient Boosting, Examples</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs054.html#___sec53" style="font-size: 80%;">Gradient Boots with Early Stopping</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs055.html#___sec54" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
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<!-- navigation toc: --> <li><a href="._DecisionTrees-bs056.html#___sec55" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
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<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
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<br>
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<p>
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<center><h4>Nov 6, 2019</h4></center> <!-- date -->
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<center><h4>Nov 7, 2019</h4></center> <!-- date -->
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<br>
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<li><a href="._DecisionTrees-bs008.html">9</a></li>
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<li><a href="._DecisionTrees-bs009.html">10</a></li>
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<li><a href="">...</a></li>
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<li><a href="._DecisionTrees-bs056.html">57</a></li>
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<li><a href="._DecisionTrees-bs001.html">&raquo;</a></li>
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</ul>
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