AI engineering glossary · Evaluation metrics
What is f-beta score?
F-beta score (also called F-beta, F2 score): A generalisation of the F1 score that weights recall beta times as much as precision: F_beta = (1 + beta^2) * P * R / (beta^2 * P + R). Beta above 1 favours recall (F2 is common), while beta below 1 favours precision (F0.5). It lets the metric reflect which error is costlier.
F-beta score: an example
For a cancer-screening model where misses are worse than false alarms, a team reports F2 (beta = 2) so that recall counts four times as much as precision in the formula's weighting.
Related terms
More Evaluation metrics terms
- F1 score
- False negative
- False negative rate
- False positive
- False positive rate
- Fleiss' kappa
- G-mean
- Gini coefficient
Where you learn it
F-beta score is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.