I'm not sure what's so wrong about the current version, but feel free to open a PR so we can make a more tangible review @daniel-yj-yang

@NicolasHug I also agree with you. I didn't found any issue with current version.

I think you're technically right, @daniel-yj-yang, for those who have been trained to use terms like "false discovery rate". This is true of a lot of the medical community, but unfortunately not for much of the machine learning community. The problem here is that a technical term is inadvertently being used: an increase in false positives will indeed decrease precision, if the number of true positives remains constant; and indeed the count of "false positives" and of "false negatives" is all that differs between the formulas for P & R. The reason for a difference between FPR and FDR is that the denominator of FDR is dependent on the estimator, whereas the denominator of FPR is dependent only on the ground truth. It is an important difference, but one that might not be easily drawn out in the context of this example. In any case, an attempt to improve the wording that avoids misuse of jargon, would be helpful.