d2_brier_score (original) (raw)

sklearn.metrics.d2_brier_score(y_true, y_proba, *, sample_weight=None, pos_label=None, labels=None)[source]#

\(D^2\) score function, fraction of Brier score explained.

Best possible score is 1.0 and it can be negative because the model can be arbitrarily worse than the null model. The null model, also known as the optimal intercept model, is a model that constantly predicts the per-class proportions of y_true, disregarding the input features. The null model gets a D^2 score of 0.0.

Read more in the User Guide.

Parameters:

y_truearray-like of shape (n_samples,)

True targets.

y_probaarray-like of shape (n_samples,) or (n_samples, n_classes)

Predicted probabilities. If y_proba.shape = (n_samples,)the probabilities provided are assumed to be that of the positive class. If y_proba.shape = (n_samples, n_classes)the columns in y_proba are assumed to correspond to the labels in alphabetical order, as done byLabelBinarizer.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

pos_labelint, float, bool or str, default=None

Label of the positive class. pos_label will be inferred in the following manner:

• if y_true in {-1, 1} or {0, 1}, pos_label defaults to 1;
• else if y_true contains string, an error will be raised andpos_label should be explicitly specified;
• otherwise, pos_label defaults to the greater label, i.e. np.unique(y_true)[-1].

labelsarray-like of shape (n_classes,), default=None

Class labels when y_proba.shape = (n_samples, n_classes). If not provided, labels will be inferred from y_true.

Returns:

d2float

The D^2 score.

References