aceThe 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…
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.
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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- Peer-reviewedBMC Genomics2020-01-02
Truvace Impact Record TRV-2026-0052, v3: “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.” Truvace, 2026-07-13. /record/TRV-2026-0052 (accessed at citation time). sha256 3e5f3320e5d38799…
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