sklearn.calibration
.CalibratedClassifierCV¶

class
sklearn.calibration.
CalibratedClassifierCV
(base_estimator=None, *, method='sigmoid', cv=None, n_jobs=None, pre_dispatch='2*n_jobs', verbose=0)[source]¶ Probability calibration with isotonic regression or logistic regression.
This class uses crossvalidation to both estimate the parameters of a classifier and subsequently calibrate a classifier. For each cv split it fits a copy of the base estimator to the training folds, and calibrates it using the testing fold. For prediction, predicted probabilities are averaged across these individual calibrated classifiers.
Already fitted classifiers can be calibrated via the parameter cv=”prefit”. In this case, no crossvalidation 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
base_estimator
if it exists, else on predict_proba.Read more in the User Guide.
 Parameters
 base_estimatorestimator instance, default=None
The classifier whose output need to be calibrated to provide more accurate
predict_proba
outputs. The default classifier is aLinearSVC
. method{‘sigmoid’, ‘isotonic’}, default=’sigmoid’
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. cvint, crossvalidation generator, iterable or “prefit”, default=None
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,StratifiedKFold
is used. Ify
is neither binary nor multiclass,KFold
is used.Refer to the 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. n_jobsint, default=None
Number of jobs to run in parallel.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors.Base estimator clones are fitted in parallel across crossvalidation iterations. Therefore parallelism happens only when cv != “prefit”.
See Glossary for more details.
New in version 0.24.
 pre_dispatchint or str, default=’2*n_jobs’
Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:
None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fastrunning jobs, to avoid delays due to ondemand spawning of the jobs
An int, giving the exact number of total jobs that are spawned
A str, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
New in version 0.24.
 verboseint, default=0
Controls the verbosity: the higher, the more messages.
New in version 0.24.
 Attributes
 classes_ndarray of 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 split, which has been fitted on training folds and calibrated on the testing 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
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_estimator=base_clf, cv=3) >>> calibrated_clf.fit(X, y) CalibratedClassifierCV(base_estimator=GaussianNB(), cv=3) >>> 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_estimator=base_clf, ... cv="prefit" ... ) >>> calibrated_clf.fit(X_calib, y_calib) CalibratedClassifierCV(base_estimator=GaussianNB(), cv='prefit') >>> 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_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.

fit
(X, y, sample_weight=None)[source]¶ Fit the calibrated model
 Parameters
 Xarraylike of shape (n_samples, n_features)
Training data.
 yarraylike of 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 of shape (n_samples, n_features)
The samples.
 Returns
 Cndarray of 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 of shape (n_samples, n_features)
The samples.
 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 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.