sklearn.metrics.roc_auc_score

sklearn.metrics.roc_auc_score(y_true, y_score)

Compute Area Under the Curve (AUC) from prediction scores

Note: this implementation is restricted to the binary classification task.

Parameters :

y_true : array, shape = [n_samples]

True binary labels.

y_score : array, shape = [n_samples]

Target scores, can either be probability estimates of the positive class, confidence values, or binary decisions.

Returns :

auc : float

See also

average_precision_score

Area under the precision-recall curve

roc_curve

Compute Receiver operating characteristic (ROC)

References

[R147]

Wikipedia entry for the Receiver operating characteristic

Examples

>>> import numpy as np
>>> from sklearn.metrics import roc_auc_score
>>> y_true = np.array([0, 0, 1, 1])
>>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> roc_auc_score(y_true, y_scores)
0.75