mars.learn.metrics.auc#
- mars.learn.metrics.auc(x, y, session=None, run_kwargs=None)[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, seeaverage_precision_score()
.- Parameters
x (tensor, shape = [n]) – x coordinates. These must be either monotonic increasing or monotonic decreasing.
y (tensor, shape = [n]) – y coordinates.
- Returns
auc
- Return type
tensor, with float value
Examples
>>> import mars.tensor as mt >>> from mars.learn import metrics >>> y = mt.array([1, 1, 2, 2]) >>> pred = mt.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