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sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC))

sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC))

Visibility: public Uploaded 31-03-2020 by Nicolas Hug sklearn==0.22.2.post1 numpy>=1.6.1 scipy>=0.9 1 runs
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  • openml-python python scikit-learn sklearn sklearn_0.22.2.post1
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Probability calibration with isotonic regression or sigmoid. See glossary entry for :term:`cross-validation estimator`. With this class, the base_estimator is fit on the train set of the cross-validation generator and the test set is used for calibration. The probabilities for each of the folds are then averaged for prediction. In case that cv="prefit" is passed to __init__, it is assumed that base_estimator has been fitted already and all data is used for calibration. Note that data for fitting the classifier and for calibrating it must be disjoint.

Components

base_estimatorsklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(1)The classifier whose output decision function needs to be calibrated to offer more accurate predict_proba outputs. If cv=prefit, the classifier must have been fit already on data

Parameters

base_estimatorThe classifier whose output decision function needs to be calibrated to offer more accurate predict_proba outputs. If cv=prefit, the classifier must have been fit already on datadefault: {"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 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 overfit Use sigmoids (Platt's calibration) in this casedefault: "sigmoid"

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