AI RESEARCH
The MCC approaches the geometric mean of precision and recall as true negatives approach infinity
arXiv CS.CV
•
ArXi:2305.00594v3 Announce Type: replace The performance of a binary classifier is described by a confusion matrix with four entries: the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). The Matthews Correlation Coefficient (MCC), F1, and Fowlkes-Mallows (FM) scores are scalars that summarize a confusion matrix. Both the F1 and FM scores are based on only three of the four entries in a confusion matrix (they ignore TN). Unlike F1 and FM, the MCC depends on all four entries of the confusion matrix, which can make it attractive in some cases.