sklearn.calibration
.calibration_curve¶

sklearn.calibration.
calibration_curve
(y_true, y_prob, normalize=False, 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.
Calibration curves may also be referred to as reliability diagrams.
Read more in the User Guide.
 Parameters
 y_truearray, shape (n_samples,)
True targets.
 y_probarray, shape (n_samples,)
Probabilities of the positive class.
 normalizebool, optional, default=False
Whether y_prob needs to be normalized into the bin [0, 1], i.e. is not a proper probability. If True, the smallest value in y_prob is mapped onto 0 and the largest one onto 1.
 n_binsint
Number of bins. A bigger number requires more data. Bins with no data points (i.e. without corresponding values in y_prob) will not be returned, thus there may be fewer than n_bins in the return value.
 strategy{‘uniform’, ‘quantile’}, (default=’uniform’)
Strategy used to define the widths of the bins.
 uniform
All bins have identical widths.
 quantile
All bins have the same number of points.
 Returns
 prob_truearray, shape (n_bins,) or smaller
The true probability in each bin (fraction of positives).
 prob_predarray, shape (n_bins,) or smaller
The mean predicted probability in each bin.
References
Alexandru NiculescuMizil 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).