F1 Score
A single number that balances precision and recall by taking their harmonic mean. It's high only when both are high, so it rewards models that are accurate and thorough at once.
Think of It Like This
A combined grade that drops if you're weak in either subject.
Because precision and recall pull against each other, the F1 score blends them into one figure between 0 and 1 — using the harmonic mean, so a low value in either one drags the whole score down. That makes it the go-to metric for imbalanced problems where plain accuracy quietly lies. When false positives and false negatives carry different costs, teams reach for a weighted Fβ variant instead.