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.

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

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

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html

doc_scikit_learn

2017-01-15 04:26:41

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