sklearn.metrics.auc¶
- sklearn.metrics.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 roc_auc_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
See also
- roc_auc_score
- Computes the area under the ROC curve
- 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
