I am working with a really imbalanced dataset ($\approx$ 1% of positive cases) for a classification problem. I know that class balancing is an important step in this scenario.
I have two questions:
Considering that I don't want to put the 0/1 label, but just to order the record according to the output score (it is always a calibrated probability of being in the positive class), is it still a good idea to do class balancing or, considering the specific output required, it is useless?
Basically, I do not care about the cut-off point, but I just sort the record in order to identify the one with a higher probability of being positives.
Considering the really small percentage of positive cases, is it better to do over/under sampling? Is there any rule-of-thumb to decide the proportion of resampling?
Thank you in advance!