Robust performance metrics for imbalanced classification problems
Hajo Holzmann Bernhard Klar
We show that established performance metrics in binary classification, such
as the F-score, the Jaccard similarity coefficient or Matthews' correlation
coefficient (MCC), are not robust to class imbalance in the sense that if the
proportion of the minority class tends to $0$, the true positive rate (TPR) of
the Bayes classifier under these metrics tends to $0$ as well. Thus, in
imbalanced classification problems, these metrics favour classifiers which
ignore the minority class. To alleviate this issue we introduce robust
modifications of the F-score and the MCC for which, even in strongly imbalanced
settings, the TPR is bounded away from $0$. We numerically illustrate the
behaviour of the various performance metrics in simulations as well as on a
credit default data set. We also discuss connections to the ROC and
precision-recall curves and give recommendations on how to combine their usage
with performance metrics.