I have a set of 10 experiments that compute precision, recall and f1-score for each experiment. Now, average precision & average recall is easy to compute. I have some confusion regarding average f1-score.
There are 2 ways on how i can compute mean f1-score:
- Take f1 scores for each of the 10 experiments and compute their average.
- Take average precision & average recall and then compute f1-score using the formula
f1 = 2*p*r/(p+r)
I could not find any strong reference to support any of the arguments. The closest document i could find is this: https://www.kaggle.com/wiki/MeanFScore
Can anyone explain with some reference (if possible) which of the methods is correct and why?
EDIT: One of the members suggested this source. Though, i still suspect the reliability of the source. I have seen people not using the method explained above in their research publications. (Even i would be using it in one of my publications) I would expect some more opinions from the community to verify this idea.