sensitivity_score#
- 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)
wheretp
is the number of true positives andfn
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.
- 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 ifaverage 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. Ifpos_label is None
and in binary classification, this function returns the average sensitivity ifaverage
is one of'weighted'
. If the data are multiclass, this will be ignored; settinglabels=[pos_label]
andaverage != '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 specifcity metric.
- specificityfloat (if
Examples
>>> 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') 0.33... >>> sensitivity_score(y_true, y_pred, average='micro') 0.33... >>> sensitivity_score(y_true, y_pred, average='weighted') 0.33... >>> sensitivity_score(y_true, y_pred, average=None) array([1., 0., 0.])