Assertion: matthew correlation coefficient | /r/HasProperty | mcc
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Source sentences:
text: The Matthews correlation coefficient (MCC) is used in machine learning as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.
text: As explained by Davide Chicco in his paper "Ten quick tips for machine learning in computational biology" (BioData Mining, 2017) and by Giuseppe Jurman in his paper "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation" (BMC Genomics, 2020), the Matthews correlation coefficient is more informative than F1 score and accuracy in evaluating binary classification problems, because it takes into account the balance ratios of the four confusion matrix categories (true positives, true negatives, false positives, false negatives) .
text: On the contrary, to avoid these dangerous misleading illusions, there is another performance score that you can exploit: the Matthews correlation coefficient  (MCC).
text: For these reasons, we strongly encourage to evaluate each test performance through the Matthews correlation coefficient (MCC), instead of the accuracy and the F1 score, for any binary classification problem.