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Science·The Trace·Automated dual reading·Published 2026-07-12

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…

TRV-2026-0052Peer-reviewedPermanent record — cite & verify
Trace impact reading

Positive state: both sides are scored from claims and sources, not community votes.

P 64The P score combines the specificity and measured human impact of the problem claim, the strength of this Trace’s sources, and problem-side source support across the same sector.G 74The G score combines the specificity and measured human impact of the gain claim, the strength of this Trace’s sources, and gain-side source support across the same sector.
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

"Army scientists energize battery research" by U.S. Army CCDC, CC BY-SA 2.0.

The quick read

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 statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets.

Main points
  • 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.
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.

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.

What this doesn’t fix

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

Sources

The debate