sklearn.metrics
.auc¶

sklearn.metrics.
auc
(x, y, reorder=’deprecated’)[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 ROCcurve, see
roc_auc_score
. For an alternative way to summarize a precisionrecall curve, seeaverage_precision_score
.Parameters:  x : array, shape = [n]
x coordinates. These must be either monotonic increasing or monotonic decreasing.
 y : array, shape = [n]
y coordinates.
 reorder : boolean, optional (default=’deprecated’)
Whether to sort x before computing. If False, assume that x must be either monotonic increasing or monotonic decreasing. If True, y is used to break ties when sorting x. Make sure that y has a monotonic relation to x when setting reorder to True.
Deprecated since version 0.20: Parameter
reorder
has been deprecated in version 0.20 and will be removed in 0.22. It’s introduced for roc_auc_score (not for general use) and is no longer used there. What’s more, the result from auc will be significantly influenced if x is sorted unexpectedly due to slight floating point error (See issue #9786). Future (and default) behavior is equivalent toreorder=False
.
Returns:  auc : float
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 precisionrecall 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