I have watched a lot of videos on machine learning and in terms of F1 scores, all are different. One video says that an F1 score of .8 is bad, but another says an F1 score of .4 is excellent. What's up with this?
I ran my model with Random Forest algorithm and got a modest average of .85 after about 5 folds. After I used my undersampling approach, I had an F1 final score of about .92-.95 after 5 folds.
If you're wondering what it was about, it was basically random numbers which was previously credit card fraud data but was replaced because of sensitive information.
Basically, my question is, what range of F1 scores are good, and is my f1 score of .92-.95 good?
(An F1 score is the harmonic mean between the precision and recall of a dataset.)