sklearn.metrics
.roc_auc_score¶

sklearn.metrics.
roc_auc_score
(y_true, y_score, average='macro', sample_weight=None, max_fpr=None)¶ 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: y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels or 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 nonthresholded 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.
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.
Will be ignored when
y_true
is binary.sample_weight : arraylike of shape = [n_samples], optional
Sample weights.
max_fpr : float > 0 and <= 1, optional
If not
None
, the standardized partial AUC [R274] over the range [0, max_fpr] is returned.Returns: auc : float
See also
average_precision_score
 Area under the precisionrecall curve
roc_curve
 Compute Receiver operating characteristic (ROC) curve
References
[R272] Wikipedia entry for the Receiver operating characteristic [R273] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8):861874. [R274] (1, 2) Analyzing a portion of the ROC curve. McClish, 1989 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