# imblearn.metrics.specificity_score¶

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

Compute the specificity

The specificity is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The specificity is intuitively the ability of the classifier to find all the positive samples.

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

Read more in the User Guide.

Parameters: y_true : ndarray, shape (n_samples, ) Ground truth (correct) target values. y_pred : ndarray, shape (n_samples, ) Estimated targets as returned by a classifier. labels : list, optional 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_label : str or int, optional (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. average : str or None, optional (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). warn_for : tuple or set, for internal use This determines which warnings will be made in the case that this function is being used to return only one of its metrics. sample_weight : ndarray, shape (n_samples, ) Sample weights. specificity : float (if average = None) or ndarray, shape (n_unique_labels, )

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')
0.66666666666666663
>>> specificity_score(y_true, y_pred, average='micro')
0.66666666666666663
>>> specificity_score(y_true, y_pred, average='weighted')
0.66666666666666663
>>> specificity_score(y_true, y_pred, average=None)
array([ 0.75,  0.5 ,  0.75])