Currently, scikit-learn only offers the
sklearn.metrics.balanced_accuracy_score (in 0.20) as metric to deal with
imbalanced datasets. The module
imblearn.metrics offers a couple of
other metrics which are used in the literature to evaluate the quality of
7.1. Sensitivity and specificity metrics¶
Sensitivity and specificity are metrics which are well known in medical imaging. Sensitivity (also called true positive rate or recall) is the proportion of the positive samples which is well classified while specificity (also called true negative rate) is the proportion of the negative samples which are well classified. Therefore, depending of the field of application, either the sensitivity/specificity or the precision/recall pair of metrics are used.
7.2. Additional metrics specific to imbalanced datasets¶
geometric_mean_score is the root of the product of class-wise
sensitivity. This measure tries to maximize the accuracy on each of the classes
while keeping these accuracies balanced.
make_index_balanced_accuracy can wrap any metric and give more
importance to a specific class using the parameter