imblearn.metrics.sensitivity_score(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None)[source]#

Compute the sensitivity.

The sensitivity is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The sensitivity quantifies the ability to avoid false negatives.

The best value is 1 and the worst value is 0.

Read more in the User Guide.

y_truearray-like of shape (n_samples,)

Ground truth (correct) target values.

y_predarray-like of shape (n_samples,)

Estimated targets as returned by a classifier.

labelsarray-like, default=None

The set of labels to include when average != 'binary', and their order if average is None. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average.

pos_labelstr, int or None, default=1

The class to report if average='binary' and the data is binary. If pos_label is None and in binary classification, this function returns the average sensitivity if average is one of 'weighted'. If the data are multiclass, this will be ignored; setting labels=[pos_label] and average != 'binary' will report scores for that label only.

averagestr, default=None

If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:


Only report results for the class specified by pos_label. This is applicable only if targets (y_{true,pred}) are binary.


Calculate metrics globally by counting the total true positives, false negatives and false positives.


Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.


Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall.


Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from accuracy_score).

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

specificityfloat (if average is None) or ndarray of shape (n_unique_labels,)

The specifcity metric.


>>> import numpy as np
>>> from imblearn.metrics import sensitivity_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> sensitivity_score(y_true, y_pred, average='macro')
>>> sensitivity_score(y_true, y_pred, average='micro')
>>> sensitivity_score(y_true, y_pred, average='weighted')
>>> sensitivity_score(y_true, y_pred, average=None)
array([1., 0., 0.])