Editing Boosting (machine learning) (section)

==Implementations==
* [[scikit-learn]], an open source machine learning library for [[Python (programming language)|Python]]
* [[Orange (software)|Orange]], a free data mining software suite, module [http://docs.orange.biolab.si/reference/rst/Orange.ensemble.html Orange.ensemble]
* [[Weka (machine learning)|Weka]] is a machine learning set of tools that offers variate implementations of boosting algorithms like AdaBoost and LogitBoost
* [[R (programming language)|R]] package [https://cran.r-project.org/web/packages/gbm/index.html GBM] (Generalized Boosted Regression Models) implements extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine.
* [https://sourceforge.net/projects/jboost/ jboost]; AdaBoost, LogitBoost, RobustBoost, Boostexter and alternating decision trees
* R package [https://cran.r-project.org/web/packages/adabag/index.html adabag]: Applies Multiclass AdaBoost.M1, AdaBoost-SAMME and Bagging
* R package [https://cran.r-project.org/web/packages/xgboost/index.html xgboost]: An implementation of gradient boosting for linear and tree-based models.