Ensemble Types#

Different base obj choices for the Ensemble are shown below The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”) The available ensembles are further broken down by which can workwith different problem_types. Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown. Also note that ensemble may require a few extra params!

binary#

“adaboost classifier”#

Base Class Documentation: sklearn.ensemble.AdaBoostClassifier

Param Distributions
“default”
defaults only

“bagging classifier”#

Base Class Documentation: sklearn.ensemble.BaggingClassifier

Param Distributions
“default”
defaults only

“balanced bagging classifier”#

Base Class Documentation: imblearn.ensemble.BalancedBaggingClassifier

Param Distributions
“default”
defaults only

“stacking classifier”#

Base Class Documentation: BPt.pipeline.ensemble_wrappers.BPtStackingClassifier

Param Distributions
“default”
defaults only

“voting classifier”#

Base Class Documentation: BPt.pipeline.ensemble_wrappers.BPtVotingClassifier

Param Distributions
“voting classifier”
voting: 'soft'

regression#

“adaboost regressor”#

Base Class Documentation: sklearn.ensemble.AdaBoostRegressor

Param Distributions
“default”
defaults only

“bagging regressor”#

Base Class Documentation: sklearn.ensemble.BaggingRegressor

Param Distributions
“default”
defaults only

“stacking regressor”#

Base Class Documentation: BPt.pipeline.ensemble_wrappers.BPtStackingRegressor

Param Distributions
“default”
defaults only

“voting regressor”#

Base Class Documentation: BPt.pipeline.ensemble_wrappers.BPtVotingRegressor

Param Distributions
“default”
defaults only

categorical#