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.
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 binary decisions.
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 : arraylike of shape = [n_samples], optional
Sample weights.
Returns: auc : float
See also
average_precision_score
 Area under the precisionrecall curve
roc_curve
 Compute Receiver operating characteristic (ROC)
References
[R179] 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