F-score

In statistical analysis of binary classification, the F-score or F-measure is a measure of a test's accuracy. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all positive results, including those not identified correctly, and the recall is the number of true positive results divided by the number of all samples that should have been identified as positive. Precision is also known as positive predictive value, and recall is also known as sensitivity in diagnostic binary classification.

Trustworthy AI Relevance

This metric addresses Robustness by quantifying relevant system properties. F-score relates to the assigned objective 'Robustness' because it quantifies the model's classification performance and therefore its consistency and reliability under typical evaluation conditions. Tracking F1 (and per-class F1) across datasets, time slices, or perturbed/noisy inputs helps detect degradation under distribution shift, class imbalance, or adversarial/noisy conditions—key aspects of robustness.