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TESTc2cbb29cd9sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)

TESTc2cbb29cd9sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)

Visibility: public Uploaded 17-10-2024 by Continuous Integration sklearn==0.23.1 numpy>=1.13.3 scipy>=0.19.1 joblib>=0.11 threadpoolctl>=2.0.0 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.23.1
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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_estimatorTESTc2cbb29cd9sklearn.tree._classes.DecisionTreeClassifier(1)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. If ``None``, then the base estimator is ``DecisionTreeClassifier(max_depth=1)``

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_`` attributes. If ``None``, then the base estimator is ``DecisionTreeClassifier(max_depth=1)``default: {"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'}, 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: 1.0
n_estimatorsThe maximum number of estimators at which boosting is terminated In case of perfect fit, the learning procedure is stopped earlydefault: 50
random_stateControls the random seed given at each `base_estimator` at each boosting iteration Thus, it is only used when `base_estimator` exposes a `random_state` Pass an int for reproducible output across multiple function calls See :term:`Glossary `.default: null

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