`sklearn.calibration`.CalibratedClassifierCV#

class sklearn.calibration.CalibratedClassifierCV(estimator=None, *, method='sigmoid', cv=None, n_jobs=None, ensemble=True)[source]#

Probability calibration with isotonic regression or logistic regression.

This class uses cross-validation to both estimate the parameters of a classifier and subsequently calibrate a classifier. With default ensemble=True, for each cv split it fits a copy of the base estimator to the training subset, and calibrates it using the testing subset. For prediction, predicted probabilities are averaged across these individual calibrated classifiers. When ensemble=False, cross-validation is used to obtain unbiased predictions, via cross_val_predict, which are then used for calibration. For prediction, the base estimator, trained using all the data, is used. This is the prediction method implemented when probabilities=True for SVC and NuSVC estimators (see User Guide for details).

Already fitted classifiers can be calibrated via the parameter cv="prefit". In this case, no cross-validation is used and all provided data is used for calibration. The user has to take care manually that data for model fitting and calibration are disjoint.

The calibration is based on the decision_function method of the estimator if it exists, else on predict_proba.

See also

calibration_curve: Compute true and predicted probabilities for a calibration curve.

References

[1]

Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers, B. Zadrozny & C. Elkan, ICML 2001

[2]

Transforming Classifier Scores into Accurate Multiclass Probability Estimates, B. Zadrozny & C. Elkan, (KDD 2002)

[3]

Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, J. Platt, (1999)

[4]

Predicting Good Probabilities with Supervised Learning, A. Niculescu-Mizil & R. Caruana, ICML 2005

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.calibration import CalibratedClassifierCV
>>> X, y = make_classification(n_samples=100, n_features=2,
...                            n_redundant=0, random_state=42)
>>> base_clf = GaussianNB()
>>> calibrated_clf = CalibratedClassifierCV(base_clf, cv=3)
>>> calibrated_clf.fit(X, y)
CalibratedClassifierCV(...)
>>> len(calibrated_clf.calibrated_classifiers_)
3
>>> calibrated_clf.predict_proba(X)[:5, :]
array([[0.110..., 0.889...],
       [0.072..., 0.927...],
       [0.928..., 0.071...],
       [0.928..., 0.071...],
       [0.071..., 0.928...]])
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_classification(n_samples=100, n_features=2,
...                            n_redundant=0, random_state=42)
>>> X_train, X_calib, y_train, y_calib = train_test_split(
...        X, y, random_state=42
... )
>>> base_clf = GaussianNB()
>>> base_clf.fit(X_train, y_train)
GaussianNB()
>>> calibrated_clf = CalibratedClassifierCV(base_clf, cv="prefit")
>>> calibrated_clf.fit(X_calib, y_calib)
CalibratedClassifierCV(...)
>>> len(calibrated_clf.calibrated_classifiers_)
1
>>> calibrated_clf.predict_proba([[-0.5, 0.5]])
array([[0.936..., 0.063...]])

Methods

`fit`(X, y[, sample_weight])	Fit the calibrated model.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict the target of new samples.
`predict_proba`(X)	Calibrated probabilities of classification.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_fit_request`(*[, sample_weight])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

fit(X, y, sample_weight=None, **fit_params)[source]#

Fit the calibrated model.

Parameters:

Xarray-like of shape (n_samples, n_features): Training data.
yarray-like of shape (n_samples,): Target values.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights. If None, then samples are equally weighted.
**fit_paramsdict: Parameters to pass to the fit method of the underlying classifier.

Returns:

selfobject: Returns an instance of self.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRouter: A MetadataRouter encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

predict(X)[source]#

Predict the target of new samples.

The predicted class is the class that has the highest probability, and can thus be different from the prediction of the uncalibrated classifier.

Parameters:

Xarray-like of shape (n_samples, n_features): The samples, as accepted by estimator.predict.

Returns:

Cndarray of shape (n_samples,): The predicted class.

predict_proba(X)[source]#

Calibrated probabilities of classification.

This function returns calibrated probabilities of classification according to each class on an array of test vectors X.

Parameters:

Xarray-like of shape (n_samples, n_features): The samples, as accepted by estimator.predict_proba.

Returns:

Cndarray of shape (n_samples, n_classes): The predicted probas.

score(X, y, sample_weight=None)[source]#

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: Mean accuracy of self.predict(X) w.r.t. y.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → CalibratedClassifierCV[source]#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → CalibratedClassifierCV[source]#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

Examples using `sklearn.calibration.CalibratedClassifierCV`#

Probability Calibration curves

Probability Calibration for 3-class classification

Probability calibration of classifiers

sklearn.calibration.CalibratedClassifierCV#

Examples using sklearn.calibration.CalibratedClassifierCV#

`sklearn.calibration`.CalibratedClassifierCV#

Examples using `sklearn.calibration.CalibratedClassifierCV`#