>> let's assume false_positive_rate = false_negative_balance

This assumption is usually wrong when trying to detect rare events (such as CC fraud or straight/gay people).

In my own experience, if you train on a prepared 50/50 dataset to some kind of reasonable accuracy, on actual data where classes are more like 96/4 you get a LOT of false positives, thus making the whole thing not very useful