sklearn.calibration.calibration_curve(y_true, y_prob, *, pos_label=None, n_bins=5, strategy='uniform')[source]

Compute true and predicted probabilities for a calibration curve.

The method assumes the inputs come from a binary classifier, and discretize the [0, 1] interval into bins.

Calibration curves may also be referred to as reliability diagrams.

Read more in the User Guide.

y_truearray-like of shape (n_samples,)

True targets.

y_probarray-like of shape (n_samples,)

Probabilities of the positive class.

pos_labelint, float, bool or str, default=None

The label of the positive class.

New in version 1.1.

n_binsint, default=5

Number of bins to discretize the [0, 1] interval. A bigger number requires more data. Bins with no samples (i.e. without corresponding values in y_prob) will not be returned, thus the returned arrays may have less than n_bins values.

strategy{‘uniform’, ‘quantile’}, default=’uniform’

Strategy used to define the widths of the bins.


The bins have identical widths.


The bins have the same number of samples and depend on y_prob.

prob_truendarray of shape (n_bins,) or smaller

The proportion of samples whose class is the positive class, in each bin (fraction of positives).

prob_predndarray of shape (n_bins,) or smaller

The mean predicted probability in each bin.


Alexandru Niculescu-Mizil and Rich Caruana (2005) Predicting Good Probabilities With Supervised Learning, in Proceedings of the 22nd International Conference on Machine Learning (ICML). See section 4 (Qualitative Analysis of Predictions).


>>> import numpy as np
>>> from sklearn.calibration import calibration_curve
>>> y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1])
>>> y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9,  1.])
>>> prob_true, prob_pred = calibration_curve(y_true, y_pred, n_bins=3)
>>> prob_true
array([0. , 0.5, 1. ])
>>> prob_pred
array([0.2  , 0.525, 0.85 ])