On reporting performance of binary classifiers

• Published in: Scientific Papers of the University of Pardubice. Series D, Faculty of Economics and Administration no. 41
• Main Authors: Škrabánek, Pavel, Doležel, Petr
• Format: Journal Article
• Language: English
• Published: Pardibuce University of Pardubice, Faculty of Economics and Administration 01.01.2017
• Subjects: Classification; Committees; Machine learning; Railroad accidents & safety
• ISSN: 1211-555X; 1804-8048

In this contribution, the question of reporting performance of binary classifiers is opened in context of the so called class imbalance problem. The class imbalance problem arises when a dataset with a highly imbalanced class distribution is used within the training or evaluation process. In such cases, only measures, which are not biased by distribution of classes in datasets, should be used; however, they cannot be chosen arbitrarily. They should be selected so that their outcomes provide desired information; and simultaneously, they should allow a full comparison of just evaluated classifier performance along, with performances of other solutions. As is shown in this article, the dilemma with reporting performance of binary classifiers can be solved using so called class balanced measures. The class balanced measures are generally applicable means, appropriate for reporting performance of binary classifiers on balanced as well as on imbalanced datasets. On the basis of the presented pieces of information, a suggestion for a generally applicable, fully-valued, reporting of binary classifiers performance is given.