 The Matthews correlation coefficient, MCC, is a better metric for evaluating binary classifications than accuracy or F1 score because it provides a more accurate representation of the performance of a model. It takes into account the size of each category in the confusion matrix, so it is less likely to produce overly optimistic results when dealing with imbalanced data sets. Additionally, MCC is more robust to outliers and is able to provide a more comprehensive evaluation of the model's performance. This article was authored by Davide Cicco and Giuseppe German.