imbalanced_ensemble.metrics.sensitivity_score(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None)

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_truendarray of shape (n_samples,)

Ground truth (correct) target values.

y_predndarray of shape (n_samples,)

Estimated targets as returned by a classifier.

labelslist, 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 or int, default=1

The class to report if average='binary' and the data is binary. 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_weightndarray 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 imbalanced_ensemble.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.])