sklearn.calibration.calibration_curve¶
- sklearn.calibration.calibration_curve(y_true, y_prob, normalize=False, n_bins=5)[source]¶
Compute true and predicted probabilities for a calibration curve.
Read more in the User Guide.
Parameters: y_true : array, shape (n_samples,)
True targets.
y_prob : array, shape (n_samples,)
Probabilities of the positive class.
normalize : bool, 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_bins : int
Number of bins. A bigger number requires more data.
Returns: prob_true : array, shape (n_bins,)
The true probability in each bin (fraction of positives).
prob_pred : array, shape (n_bins,)
The mean predicted probability in each bin.
References
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).
