det_curve (original) (raw)
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.
Added 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 non-thresholded measure of decisions (as returned by “decision_function” on some classifiers). For decision_function scores, values greater than or equal to zero should indicate the positive class.
pos_labelint, float, bool or str, default=None
The label of the positive class. When pos_label=None, if y_true is in {-1, 1} or {0, 1},pos_label is set to 1, otherwise an error will be raised.
sample_weightarray-like 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 probability or fall-out.
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.
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 ])