# Why PR score is down when balanced accuracy is good?

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