API Reference / algorithms /

ensemble/

Ensemble (meta-learning) algorithms.

Meta-learners that combine multiple base estimators to improve performance, stability, and flexibility.

Available algorithms

  • AdaBoostClassifier: Adaptive boosting for multiclass classification.
  • GradientBoostingRegressor: Gradient boosting for regression.
  • BaggingClassifier: Bootstrap aggregating for classification.
  • BaggingRegressor: Bootstrap aggregating for regression.
  • OneVsRestClassifier: Handles multi-class problems via binary decomposition.
  • StackingClassifier: Combines classifiers using a meta-learner.
  • StackingRegressor: Combines regressors using a meta-learner.
  • VotingClassifier: Combines classifiers using various voting rules.
  • VotingRegressor: Combines regressors using various aggregation rules.

Modules

adaboost

AdaBoostClassifier classifier implementation.

2 classes · 0 functions
bagging

Bagging (Bootstrap Aggregating) ensemble implementations for classification and regression.

2 classes · 0 functions
gradient_boosting_regressor

GradientBoostingRegressor implementation.

1 class · 0 functions
one_vs_rest

OneVsRestClassifier implementation.

1 class · 0 functions
stacking

Stacking (Stacked Generalization) ensemble implementations for classification and regression.

2 classes · 0 functions
voting

Voting ensemble implementations for classification and regression.

2 classes · 0 functions