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.
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 : array-like of shape = [n_samples], optional
Sample weights.
Returns: auc : float
See also
- average_precision_score
- Area under the precision-recall curve
- roc_curve
- Compute Receiver operating characteristic (ROC)
References
[R177] 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