sklearn.metrics.roc_auc_score()
sklearn.metrics.roc_auc_score
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sklearn.metrics.roc_auc_score(y_true, y_score, average='macro', sample_weight=None)
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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':
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Calculate metrics globally by considering each element of the label indicator matrix as a label.
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'macro':
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Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
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'weighted':
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Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label).
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'samples':
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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
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average_precision_score
- Area under the precision-recall curve
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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
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Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html