The F score ranks diagnostic tests and prediction models inconsistently with their clinical utility.

ABSTRACT:

Background

The F score is derived from precision (positive predictive value) and recall (sensitivity). It is increasingly used to evaluate diagnostic tests and prediction models in the machine learning literature. Although precision and recall can be differentially weighted using a parameter β, almost all applications use equal weighting, with β set to 1.

Methods

We considered a cancer detection scenario to explore the properties of the F score in comparison to net benefit, a well-established method for evaluating the clinical utility of tests and models. Because missing cancer can be fatal and biopsies are an invasive procedure, we would favor a test with high sensitivity.

Results

F scores did not provide a rank ordering of tests consistent with utility. F1 was highe

SUBMITTER: Assel M 

PROVIDER: S-EPMC12683848 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature