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sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.ExtraTreeClassifier)

sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.ExtraTreeClassifier)

Visibility: public Uploaded 14-08-2021 by Sergey Redyuk sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.18.1
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An AdaBoost classifier. An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases. This class implements the algorithm known as AdaBoost-SAMME [2].

Components

base_estimatorsklearn.tree.tree.ExtraTreeClassifier(28)The base estimator from which the boosted ensemble is built Support for sample weighting is required, as well as proper `classes_` and `n_classes_` attributes

Parameters

algorithmdefault: "SAMME.R"
base_estimatorThe base estimator from which the boosted ensemble is built Support for sample weighting is required, as well as proper `classes_` and `n_classes_` attributesdefault: {"oml-python:serialized_object": "component_reference", "value": {"key": "base_estimator", "step_name": null}}
learning_rateLearning rate shrinks the contribution of each classifier by ``learning_rate``. There is a trade-off between ``learning_rate`` and ``n_estimators`` algorithm : {'SAMME', 'SAMME.R'}, optional (default='SAMME.R') If 'SAMME.R' then use the SAMME.R real boosting algorithm ``base_estimator`` must support calculation of class probabilities If 'SAMME' then use the SAMME discrete boosting algorithm The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterationsdefault: 0.003
n_estimatorsThe maximum number of estimators at which boosting is terminated In case of perfect fit, the learning procedure is stopped earlydefault: 512
random_stateIf int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.default: 3

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