sklearn.ensemble #

Ensemble-based methods for classification, regression and anomaly detection.

User guide. See theEnsembles: Gradient boosting, random forests, bagging, voting, stacking section for further details.

`AdaBoostClassifier`	An AdaBoost classifier.
`AdaBoostRegressor`	An AdaBoost regressor.
`BaggingClassifier`	A Bagging classifier.
`BaggingRegressor`	A Bagging regressor.
`ExtraTreesClassifier`	An extra-trees classifier.
`ExtraTreesRegressor`	An extra-trees regressor.
`GradientBoostingClassifier`	Gradient Boosting for classification.
`GradientBoostingRegressor`	Gradient Boosting for regression.
`HistGradientBoostingClassifier`	Histogram-based Gradient Boosting Classification Tree.
`HistGradientBoostingRegressor`	Histogram-based Gradient Boosting Regression Tree.
`IsolationForest`	Isolation Forest Algorithm.
`RandomForestClassifier`	A random forest classifier.
`RandomForestRegressor`	A random forest regressor.
`RandomTreesEmbedding`	An ensemble of totally random trees.
`StackingClassifier`	Stack of estimators with a final classifier.
`StackingRegressor`	Stack of estimators with a final regressor.
`VotingClassifier`	Soft Voting/Majority Rule classifier for unfitted estimators.
`VotingRegressor`	Prediction voting regressor for unfitted estimators.

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