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).

Examples using sklearn.calibration.calibration_curve

doc_scikit_learn
2017-01-15 04:25:31
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