sklearn.calibration
.CalibratedClassifierCV¶

class
sklearn.calibration.
CalibratedClassifierCV
(base_estimator=None, *, method='sigmoid', cv=None)[source]¶ Probability calibration with isotonic regression or logistic regression.
The calibration is based on the decision_function method of the
base_estimator
if it exists, else on predict_proba.Read more in the User Guide.
 Parameters
 base_estimatorinstance BaseEstimator
The classifier whose output need to be calibrated to provide more accurate
predict_proba
outputs. method‘sigmoid’ or ‘isotonic’
The 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 nonparametric approach. It is not advised to use isotonic calibration with too few calibration samples
(<<1000)
since it tends to overfit. cvinteger, crossvalidation generator, iterable or “prefit”, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
None, to use the default 5fold crossvalidation,
integer, to specify the number of folds.
An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if
y
is binary or multiclass,sklearn.model_selection.StratifiedKFold
is used. Ify
is neither binary nor multiclass,sklearn.model_selection.KFold
is used.Refer User Guide for the various crossvalidation 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.Changed in version 0.22:
cv
default value if None changed from 3fold to 5fold.
 Attributes
 classes_array, shape (n_classes)
The class labels.
 calibrated_classifiers_list (len() equal to cv or 1 if cv == “prefit”)
The list of calibrated classifiers, one for each crossvalidation fold, which has been fitted on all but the validation fold and calibrated on the validation fold.
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. NiculescuMizil & R. Caruana, ICML 2005
Methods
fit
(X, y[, sample_weight])Fit the calibrated model
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict the target of new samples.
Posterior probabilities of classification
score
(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(**params)Set the parameters of this estimator.

__init__
(base_estimator=None, *, method='sigmoid', cv=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(X, y, sample_weight=None)[source]¶ Fit the calibrated model
 Parameters
 Xarraylike, shape (n_samples, n_features)
Training data.
 yarraylike, shape (n_samples,)
Target values.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted.
 Returns
 selfobject
Returns an instance of self.

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
 paramsmapping of string to any
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
 Xarraylike, shape (n_samples, n_features)
The samples.
 Returns
 Carray, shape (n_samples,)
The predicted class.

predict_proba
(X)[source]¶ Posterior probabilities of classification
This function returns posterior probabilities of classification according to each class on an array of test vectors X.
 Parameters
 Xarraylike, shape (n_samples, n_features)
The samples.
 Returns
 Carray, 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 multilabel 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
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
Mean accuracy of self.predict(X) wrt. y.

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). 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
 selfobject
Estimator instance.