F1 score
The harmonic mean of precision and recall, collapsing both into one number. It punishes a bad score on either far more than an arithmetic mean would.
What people get wrong
It weights precision and recall equally, which is almost never what the problem actually costs. Where one error is far worse than the other, report both separately or weight them deliberately.
A definition is not an understanding. This is taught properly in Choosing the metric that matches the cost of being wrong, part of Model evaluation & metrics.
Learn this properlyTerms from the same topic