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TRV-2026-0052Certified recordPeer-reviewed

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 F 1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these stati…

Science · The Trace — both readings · certified 2026-07-12 · v2 · article view · machine-readable

Current reading — gain

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation: Accuracy and F 1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks.

Current reading — problem

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation: However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets.

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Historical research candidate. An editor must verify study design, population, effect size, and whether later evidence changes the reading before publication.

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Truvace Impact Record TRV-2026-0052, v2: “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.” Truvace, 2026-07-12. /record/TRV-2026-0052 (accessed at citation time). sha256 492ba3565dcbf8da…

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