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sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)

sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)

Visibility: public Uploaded 13-08-2021 by Sergey Redyuk sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9 6 runs
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  • openml-python python scikit-learn sklearn sklearn_0.18.1
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Soft Voting/Majority Rule classifier for unfitted estimators. .. versionadded:: 0.17

Parameters

estimatorsInvoking the ``fit`` method on the ``VotingClassifier`` will fit clones of those original estimators that will be stored in the class attribute `self.estimators_`default: [{"oml-python:serialized_object": "component_reference", "value": {"key": "DecisionTreeClassifier", "step_name": "DecisionTreeClassifier"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "ExtraTreeClassifier", "step_name": "ExtraTreeClassifier"}}]
n_jobsThe number of jobs to run in parallel for ``fit`` If -1, then the number of jobs is set to the number of cores.default: 1
votingIf 'hard', uses predicted class labels for majority rule voting Else if 'soft', predicts the class label based on the argmax of the sums of the predicted probabilities, which is recommended for an ensemble of well-calibrated classifiersdefault: "hard"
weightsSequence of weights (`float` or `int`) to weight the occurrences of predicted class labels (`hard` voting) or class probabilities before averaging (`soft` voting). Uses uniform weights if `None`default: null

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