Calculate the F-beta score with a configurable weight between precision and recall.
The F-beta score generalizes F1 into a weighted harmonic mean of precision and recall: F_β = (1 + β²) × (P × R) / (β² × P + R). Setting β = 1 recovers the standard F1 score (equal weight). Setting β > 1 (commonly β = 2) weighs recall more heavily than precision, useful when missing positives is costlier than false alarms. Setting β < 1 (commonly β = 0.5) weighs precision more heavily, useful when false positives are costlier.
F_beta = (1 + beta^2) * (precision * recall) / (beta^2 * precision + recall)
F0.5 weighs precision twice as much as recall, F1 weighs them equally, and F2 weighs recall twice as much as precision — choose based on which error type (false positive vs. false negative) is more costly for your use case.
F2 is common in domains like medical screening or fraud detection where missing a true positive (a false negative) is much worse than a false alarm, so recall is weighted more heavily in the combined score.
Yes — first compute precision = TP/(TP+FP) and recall = TP/(TP+FN) using the Precision and Recall Calculators, then enter those values here.