sklearn.metrics.roc_auc_score

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

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