The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

Abstract

In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F₁ score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F₁ score in evaluating binary classification tasks by all scientific communities.