sklearn.metrics.roc_auc_score(y_true, y_score, average=’macro’, sample_weight=None, max_fpr=None)
[source]
Compute Area Under the Receiver Operating Characteristic Curve (ROC 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: 


Returns: 

See also
average_precision_score
roc_curve
[1]  Wikipedia entry for the Receiver operating characteristic 
[2]  Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8):861874. 
[3]  (1, 2) Analyzing a portion of the ROC curve. McClish, 1989 
>>> 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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