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TEST3f2bf43ee3sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)

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sklearn==1.3.2
numpy>=1.17.3
scipy>=1.5.0
joblib>=1.1.1
threadpoolctl>=2.0.0 0 runs

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base_estimator | TEST3f2bf43ee3sklearn.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 :class:`~sklearn.tree.DecisionTreeClassifier` initialized with `max_depth=1` .. deprecated:: 1.2 `base_estimator` is deprecated and will be removed in 1.4 Use `estimator` instead. |

algorithm | default: "SAMME.R" | |

base_estimator | 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 :class:`~sklearn.tree.DecisionTreeClassifier` initialized with `max_depth=1` .. deprecated:: 1.2 `base_estimator` is deprecated and will be removed in 1.4 Use `estimator` instead. | default: {"oml-python:serialized_object": "component_reference", "value": {"key": "base_estimator", "step_name": null}} |

estimator | 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 :class:`~sklearn.tree.DecisionTreeClassifier` initialized with `max_depth=1` .. versionadded:: 1.2 `base_estimator` was renamed to `estimator` | default: null |

learning_rate | Weight applied to each classifier at each boosting iteration. A higher learning rate increases the contribution of each classifier. There is a trade-off between the `learning_rate` and `n_estimators` parameters Values must be in the range `(0.0, inf)` algorithm : {'SAMME', 'SAMME.R'}, default='SAMME.R' If 'SAMME.R' then use the SAMME.R real boosting algorithm ``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 iterations | default: 1.0 |

n_estimators | The maximum number of estimators at which boosting is terminated In case of perfect fit, the learning procedure is stopped early Values must be in the range `[1, inf)` | default: 50 |

random_state | Controls the random seed given at each `estimator` at each
boosting iteration
Thus, it is only used when `estimator` exposes a `random_state`
Pass an int for reproducible output across multiple function calls
See :term:`Glossary | default: null |

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