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

ABSTRACT:

Background

To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F₁ score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets.

Results

The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good

SUBMITTER: Chicco D 

PROVIDER: S-EPMC6941312 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature