det_curve

sklearn.metrics.det_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=False)[source]

Compute Detection Error Tradeoff (DET) for different probability thresholds.

Note: Support beyond binary classification tasks, via one-vs-rest or one-vs-one, is not implemented.

The DET curve is used for evaluation of ranking and error tradeoffs in binary classification tasks.

Read more in the User Guide.

Added in version 0.24.

Changed in version 1.7: An arbitrary threshold at infinity is added to represent a classifier that always predicts the negative class, i.e. fpr=0 and fnr=1, unless fpr=0 is already reached at a finite threshold.

Parameters:

y_truearray-like 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_scorearray-like of shape of (n_samples,)

Target scores, can either be probability estimates of the positive class or non-thresholded decision values (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.

drop_intermediatebool, default=False

Whether to drop thresholds where true positives (tp) do not change from the previous or subsequent threshold. All points with the same tp value have the same fnr and thus same y coordinate.

Added in version 1.7.

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 thresholds on the decision function (either predict_proba or decision_function) used to compute FPR and FNR.

Changed in version 1.7: An arbitrary threshold at infinity is added for the case fpr=0 and fnr=1.

See also

DetCurveDisplay.from_estimator

Plot DET curve given an estimator and some data.

DetCurveDisplay.from_predictions

Plot DET curve given the true and predicted labels.

DetCurveDisplay

DET curve visualization.

roc_curve

Compute Receiver operating characteristic (ROC) curve.

precision_recall_curve

Compute precision-recall curve.

confusion_matrix_at_thresholds

For binary classification, compute true negative, false positive, false negative and true positive counts per threshold.

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 ])

Gallery examples

Evaluate the performance of a classifier with Confusion Matrix

Detection error tradeoff (DET) curve