What is precision, recall, and F1-score?

Precision = TP / (TP + FP) — of all predicted positives, how many were actually positive. High precision means few false alarms. Recall (Sensitivity) = TP / (TP + FN) — of all actual positives, how many were correctly identified. High recall means few misses. There is a tradeoff: increasing one typically decreases the other. The F1-Score = 2 × (Precision × Recall) / (Precision + Recall) — harmonic mean, balancing both. F1 is preferred over accuracy when classes are imbalanced.