I just want to note the following paper, published this year, that proposes "a simple transformation of the F-measure, which we call $F^*$ (F-star), which has an immediate practical interpretation." It even cited this very discussion on Cross Validated.

Specifically, $F^* = F/(2-F)$ "is the proportion of the relevant classifications which are correct, where a relevant classification is one which is either really class 1 or classified as class 1".

REFERENCES:

• Hand, D.J., Christen, P. & Kirielle, N., "F*: an interpretable transformation of the F-measure", Machine Learning 110, 451–456 (2021). https://doi.org/10.1007/s10994-021-05964-1

I just want to note the following paper, published this year, that proposes "a simple transformation of the F-measure, which [the authors] call $F^*$ (F-star), which has an immediate practical interpretation." It even cited this very discussion on Cross Validated.

Specifically, $F^* = F/(2-F)$ "is the proportion of the relevant classifications which are correct, where a relevant classification is one which is either really class 1 or classified as class 1".

REFERENCES:

• Hand, D.J., Christen, P. & Kirielle, N., "F*: an interpretable transformation of the F-measure", Machine Learning 110, 451–456 (2021). https://doi.org/10.1007/s10994-021-05964-1