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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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base_estimator | sklearn.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 |

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 | default: {"oml-python:serialized_object": "component_reference", "value": {"key": "base_estimator", "step_name": null}} |

learning_rate | Learning 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 iterations | default: 0.003 |

n_estimators | The maximum number of estimators at which boosting is terminated In case of perfect fit, the learning procedure is stopped early | default: 512 |

random_state | If 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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