I just read this discussion here and here.
I have a dataset of 977 records where class proportion is 77:23.
My balanced accuracy is 75.5, AUC is 81% but my average_precision_score is only 56%.
Does it make sense to use PR score for my dataset? Is it heavily imbalanced?
Actually, in our domain, missing a positive case is costly. So, in order to identify all positives correctly, our model does make some mistakes (false positives).
but am not sure what should I decide based on PR score? Is my model useless?
but my AUC, Lift and gain charts show some degree of seperation between classes etc
