sklearn.metrics.auc_score¶

sklearn.metrics.auc_score(*args, **kwargs)¶

DEPRECATED: Function ‘auc_score’ has been renamed to ‘roc_auc_score’ and will be removed in release 0.16.

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)

Examples

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