sklearn.metrics.roc_auc_score

sklearn.metrics.roc_auc_score(y_true, y_score, average='macro', sample_weight=None) [source]

Compute Area Under the Curve (AUC) from prediction scores

Note: this implementation is restricted to the binary classification task or multilabel classification task in label indicator format.

Read more in the User Guide.

Parameters:

y_true : array, shape = [n_samples] or [n_samples, n_classes] True binary labels in binary label indicators. y_score : array, shape = [n_samples] or [n_samples, n_classes] Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers). average : string, [None, ‘micro’, ‘macro’ (default), ‘samples’, ‘weighted’] If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: 'micro': Calculate metrics globally by considering each element of the label indicator matrix as a label. 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. 'weighted': Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). 'samples': Calculate metrics for each instance, and find their average. sample_weight : array-like of shape = [n_samples], optional Sample weights.

Returns:

auc : float

See also

average_precision_score

Area under the precision-recall curve

roc_curve

Compute Receiver operating characteristic (ROC)

References

[R224]

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