specificity_score

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

Compute the specificity.

The specificity is the ratio tn / (tn + fp) where tn is the number of true negatives and fp the number of false positives. The specificity quantifies the ability to avoid false positives.

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

Read more in the User Guide.

Parameters:

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 specificity 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:

'binary':

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

'micro':

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

'macro':

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

'weighted':

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.

'samples':

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.

Returns:

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

The specificity metric.

Examples

>>> import numpy as np
>>> from imblearn.metrics import specificity_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> specificity_score(y_true, y_pred, average='macro')
0.66...
>>> specificity_score(y_true, y_pred, average='micro')
0.66...
>>> specificity_score(y_true, y_pred, average='weighted')
0.66...
>>> specificity_score(y_true, y_pred, average=None)
array([0.75, 0.5 , 0.75])