sklearn.metrics
.roc_auc_score¶

sklearn.metrics.
roc_auc_score
(y_true, y_score, average='macro', sample_weight=None, max_fpr=None, multi_class='raise', labels=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
 y_truearray, shape = [n_samples] or [n_samples, n_classes]
True binary labels or binary label indicators. The multiclass case expects shape = [n_samples] and labels with values in
range(n_classes)
. y_scorearray, 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. The multiclass case expects shape = [n_samples, n_classes] where the scores correspond to probability estimates.
 averagestring, [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: Note: multiclass ROC AUC currently only handles the ‘macro’ and ‘weighted’ averages.'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_weightarraylike of shape = [n_samples], optional
Sample weights.
 max_fprfloat > 0 and <= 1, optional
If not
None
, the standardized partial AUC [3] over the range [0, max_fpr] is returned. For the multiclass case,max_fpr
, should be either equal toNone
or1.0
as AUC ROC partial computation currently is not supported for multiclass. multi_classstring, ‘ovr’ or ‘ovo’, optional(default=’raise’)
Determines the type of multiclass configuration to use.
multi_class
must be provided wheny_true
is multiclass.'ovr'
:Calculate metrics for the multiclass case using the onevsrest approach.
'ovo'
:Calculate metrics for the multiclass case using the onevsone approach.
 labelsarray, shape = [n_classes] or None, optional (default=None)
List of labels to index
y_score
used for multiclass. IfNone
, the lexicon order ofy_true
is used to indexy_score
.
 Returns
 aucfloat
See also
average_precision_score
Area under the precisionrecall curve
roc_curve
Compute Receiver operating characteristic (ROC) curve
References
 1
 2
Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8):861874.
 3
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