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
sample_weight : array-like of shape = [n_samples], optional Sample weights. |
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Returns: |
auc : float |
See also
average_precision_score
roc_curve
[R224] | Wikipedia entry for the Receiver operating characteristic |
>>> 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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