Visibility: public Uploaded 31-03-2020 by Nicolas Hug sklearn==0.23.dev0 numpy>=1.6.1 scipy>=0.9 63 runs
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  • openml-python python scikit-learn sklearn sklearn_0.23.dev0
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Probability calibration with isotonic regression or logistic regression. The calibration is based on the :term:`decision_function` method of the `base_estimator` if it exists, else on :term:`predict_proba`.


base_estimatorsklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(2)The classifier whose output need to be calibrated to provide more accurate `predict_proba` outputs


base_estimatorThe classifier whose output need to be calibrated to provide more accurate `predict_proba` outputsdefault: {"oml-python:serialized_object": "component_reference", "value": {"key": "base_estimator", "step_name": null}}
cvDetermines the cross-validation splitting strategy Possible inputs for cv are: - None, to use the default 5-fold cross-validation, - integer, to specify the number of folds - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices For integer/None inputs, if ``y`` is binary or multiclass, :class:`sklearn.model_selection.StratifiedKFold` is used. If ``y`` is neither binary nor multiclass, :class:`sklearn.model_selection.KFold` is used Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here If "prefit" is passed, it is assumed that `base_estimator` has been fitted already and all data is used for calibration .. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold.default: 5
methodThe method to use for calibration. Can be 'sigmoid' which corresponds to Platt's method (i.e. a logistic regression model) or 'isotonic' which is a non-parametric approach. It is not advised to use isotonic calibration with too few calibration samples ``(<<1000)`` since it tends to overfitdefault: "sigmoid"


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