AUC(area under the curve)ROC曲线下方的面积
auc(x, y, reorder=False)
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 :func:`roc_auc_score`. For an alternative
way to summarize a precision-recall curve, see
:func:`average_precision_score`.
Parameters
----------
x : array, shape = [n]
x coordinates.
y : array, shape = [n]
y coordinates.
reorder : boolean, optional (default=False)
If True, assume that the curve is ascending in the case of ties, as for
an ROC curve. If the curve is non-ascending, the result will be wrong.
Returns
-------
auc : float
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
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
