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TEST3b65997606sklearn.ensemble._bagging.BaggingClassifier(base_estimator=sklearn.ensemble._bagging.BaggingClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier))

Visibility: public Uploaded 15-01-2024 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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base_estimator | TEST3b65997606sklearn.ensemble._bagging.BaggingClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(1) | The base estimator to fit on random subsets of the dataset If None, then the base estimator is a decision tree |

base_estimator | The base estimator to fit on random subsets of the dataset If None, then the base estimator is a decision tree | default: {"oml-python:serialized_object": "component_reference", "value": {"key": "base_estimator", "step_name": null}} |

bootstrap | Whether samples are drawn with replacement. If False, sampling without replacement is performed | default: true |

bootstrap_features | Whether features are drawn with replacement | default: false |

max_features | The number of features to draw from X to train each base estimator ( without replacement by default, see `bootstrap_features` for more details) - If int, then draw `max_features` features - If float, then draw `max_features * X.shape[1]` features | default: 1.0 |

max_samples | The number of samples to draw from X to train each base estimator (with replacement by default, see `bootstrap` for more details) - If int, then draw `max_samples` samples - If float, then draw `max_samples * X.shape[0]` samples | default: 1.0 |

n_estimators | The number of base estimators in the ensemble | default: 10 |

n_jobs | The number of jobs to run in parallel for both :meth:`fit` and
:meth:`predict`. ``None`` means 1 unless in a
:obj:`joblib.parallel_backend` context. ``-1`` means using all
processors. See :term:`Glossary | default: null |

oob_score | Whether to use out-of-bag samples to estimate the generalization error | default: false |

random_state | Controls the random resampling of the original dataset
(sample wise and feature wise)
If the base estimator accepts a `random_state` attribute, a different
seed is generated for each instance in the ensemble
Pass an int for reproducible output across multiple function calls
See :term:`Glossary | default: null |

verbose | Controls the verbosity when fitting and predicting. | default: 0 |

warm_start | When set to True, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit
a whole new ensemble. See :term:`the Glossary | default: false |

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