sklearn.metrics
.det_curve¶

sklearn.metrics.
det_curve
(y_true, y_score, pos_label=None, sample_weight=None)[source]¶ Compute error rates for different probability thresholds.
Note
This metric is used for evaluation of ranking and error tradeoffs of a binary classification task.
Read more in the User Guide.
New in version 0.24.
 Parameters
 y_truendarray of shape (n_samples,)
True binary labels. If labels are not either {1, 1} or {0, 1}, then pos_label should be explicitly given.
 y_scorendarray of shape of (n_samples,)
Target scores, can either be probability estimates of the positive class, confidence values, or nonthresholded measure of decisions (as returned by “decision_function” on some classifiers).
 pos_labelint or str, default=None
The label of the positive class. When
pos_label=None
, ify_true
is in {1, 1} or {0, 1},pos_label
is set to 1, otherwise an error will be raised. sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 fprndarray of shape (n_thresholds,)
False positive rate (FPR) such that element i is the false positive rate of predictions with score >= thresholds[i]. This is occasionally referred to as false acceptance propability or fallout.
 fnrndarray of shape (n_thresholds,)
False negative rate (FNR) such that element i is the false negative rate of predictions with score >= thresholds[i]. This is occasionally referred to as false rejection or miss rate.
 thresholdsndarray of shape (n_thresholds,)
Decreasing score values.
See also
plot_det_curve
Plot detection error tradeoff (DET) curve.
DetCurveDisplay
DET curve visualization.
roc_curve
Compute Receiver operating characteristic (ROC) curve.
precision_recall_curve
Compute precisionrecall curve.
Examples
>>> import numpy as np >>> from sklearn.metrics import det_curve >>> y_true = np.array([0, 0, 1, 1]) >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8]) >>> fpr, fnr, thresholds = det_curve(y_true, y_scores) >>> fpr array([0.5, 0.5, 0. ]) >>> fnr array([0. , 0.5, 0.5]) >>> thresholds array([0.35, 0.4 , 0.8 ])