`sklearn.metrics`.auc¶

sklearn.metrics.auc(x, y)[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. For an alternative way to summarize a precision-recall curve, see average_precision_score.

Parameters

xarray, shape = [n]: x coordinates. These must be either monotonic increasing or monotonic decreasing.
yarray, shape = [n]: y coordinates.

Returns

aucfloat

See also

roc_auc_score: Compute the area under the ROC curve
average_precision_score: Compute average precision from prediction scores
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

sklearn.metrics.auc¶

`sklearn.metrics`.auc¶