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TRV-2026-0052Version 3 · Retracted

Written 2026-07-13 00:39:15 UTC · current record

Reason for this version

Model backfill: source did not support a publishable AI-impact claim

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TRUVACE RECORD VERSION
record: TRV-2026-0052
version: 3
kind: retracted
reason: Model backfill: source did not support a publishable AI-impact claim
timestamp: 2026-07-13T00:39:15.638183Z
status: archived
lens: trace
sector: science
headline: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
dek: 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…
gain_title: 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_title: 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.
trace_subject: (none)
gain_reading: 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_reading: 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.
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.
limitation: Historical research candidate. An editor must verify study design, population, effect size, and whether later evidence changes the reading before publication.
tag: Automated dual reading
key_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.
rundown: 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.
sources:
- peer_reviewed | BMC Genomics | https://doi.org/10.1186/s12864-019-6413-7 | 2020-01-02
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