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

Species distribution modeling

Species distribution modeling
Poisson regression and non-normal loss

Poisson regression and non-normal loss

Poisson regression and non-normal loss
Tweedie regression on insurance claims

Tweedie regression on insurance claims

Tweedie regression on insurance claims
Multiclass Receiver Operating Characteristic (ROC)

Multiclass Receiver Operating Characteristic (ROC)

Multiclass Receiver Operating Characteristic (ROC)
Precision-Recall

Precision-Recall

Precision-Recall
Receiver Operating Characteristic (ROC) with cross validation

Receiver Operating Characteristic (ROC) with cross validation

Receiver Operating Characteristic (ROC) with cross validation