`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-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.

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

Examples using `sklearn.metrics.auc`#

Species distribution modeling

Poisson regression and non-normal loss

Tweedie regression on insurance claims

Multiclass Receiver Operating Characteristic (ROC)

Precision-Recall

Receiver Operating Characteristic (ROC) with cross validation

sklearn.metrics.auc#

Examples using sklearn.metrics.auc#

`sklearn.metrics`.auc#

Examples using `sklearn.metrics.auc`#