metrics.
roc_auc_score
metrics.roc_auc_score(y_true, y_score, pos_label=None, sample_weight=None)
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
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Parameters:
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y_true : array, shape = [n_samples] or [n_samples, n_classes]
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True binary _labels or binary label indicators.
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y_score : array, shape = [n_samples] or [n_samples, n_classes]
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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). For binary
y_true, y_score is supposed to be the score of the class with greater
label.
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sample_weight : array-like of shape = [n_samples], optional
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Sample weights.
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Returns:
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auc : float
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