auc (original) (raw)

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, seeaverage_precision_score.

Parameters:

xarray-like of shape (n,)

X coordinates. These must be either monotonic increasing or monotonic decreasing.

yarray-like of shape (n,)

Y coordinates.

Returns:

aucfloat

Area Under the Curve.

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) np.float64(0.75)

Gallery examples#