sklearn.metrics.auc(x, y, reorder=’deprecated’)
[source]
Compute Area Under the Curve (AUC) using the trapezoidal rule
This is a general function, given points on a curve. For computing the area under the ROCcurve, see roc_auc_score
. For an alternative way to summarize a precisionrecall curve, see average_precision_score
.
Parameters: 


Returns: 

See also
roc_auc_score
average_precision_score
precision_recall_curve
>>> import numpy as np >>> from sklearn import metrics >>> y = np.array([1, 1, 2, 2]) >>> pred = np.array([0.1, 0.4, 0.35, 0.8]) >>> fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label=2) >>> metrics.auc(fpr, tpr) 0.75
sklearn.metrics.auc
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Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.metrics.auc.html