sklearn.metrics.auc()

sklearn.metrics.auc(x, y, reorder=False) [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 ROC-curve, see roc_auc_score.

Parameters:	x : array, shape = [n] x coordinates. y : array, shape = [n] y coordinates. reorder : boolean, optional (default=False) If True, assume that the curve is ascending in the case of ties, as for an ROC curve. If the curve is non-ascending, the result will be wrong.
Returns:	auc : float

See also

roc_auc_score

Computes the area under the ROC curve

precision_recall_curve

Compute precision-recall pairs for different probability thresholds

Examples

>>> 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

Examples using sklearn.metrics.auc

Species distribution modeling

Sparse recovery: feature selection for sparse linear models

Receiver Operating Characteristic (ROC)

Receiver Operating Characteristic (ROC) with cross validation