F1 is the harmonic mean of precision and recall, computed as two times their product divided by their sum. A plain average would let strong precision cover for weak recall. The harmonic mean will not; it sits close to the smaller of the two numbers.
Precision 1.0 with recall 0.1 gives an F1 near 0.18, not 0.55. That gap is exactly what stops a model from scoring well by flagging one safe case.
F1 helps when you need one number to rank models or pick a cutoff, and the positive class is rare. It refuses to reward a model that games one side.
The cost is what it assumes. It weights precision and recall equally, which rarely matches real error costs, and it ignores true negatives entirely. When one side matters more, use F-beta, which lets you weight recall up or down.
This answer doesn't lend itself to a diagram - it reads best . No credits were charged.
Why there's no diagram: “”
The interactive diagram is below the answer - jump to diagram ↓ · Below it, the related concept . Jump to it ↓
The diagram below the answer is the concept . Jump to it ↓