Commonly refers to the $F_1$ score, however, it’s important to note that the F measure can be weighted towards higher recall than precision (e.g. the $F_2$ score) or higher precision (e.g. $F_{0.5}$). The general formula is $F_\beta = (1+\beta^2) \cdot \frac{precision \cdot recall}{(\beta^2 \cdot precision) + recall}$.