So I came across a bioinformatics paper, where I found a line which says:
One potential problem with using a training set with equal numbers of positive and negative examples in cross-validation is that it can artificially inflate performance estimates because the number of false-positive classifications is proportional to the number of examples classified. So applying these methods to all proteins in an organism may result in a large number of false-positive identifications.
I am unable to understand how classification of balanced dataset is a problem. Can someone please explain this to me?