Which class to define as positive in unbalanced classification In a classification task, why do we usually choose the minority class as "positive" case for response variable? For example, if there are 1000, 9000 for class A and B respectively, we usually define class A as 1 and class B as 0, thus the response variable can have average as 0.1 instead of 0.9. If the response variable has average like 0.6 or 0.7, will there be any issues for the classification algorithm? Do we need to choose the class with average 0.4 or 0.3 as positive class? Thanks in advance!

It does not matter. It is done that way because it would be something like disease detection where most people don’t have the disease, but that’s what you want to catch. However, all of the math works out fine either way. If you made the heathy class the $1$s, your model would be outputting the probability of being healthy instead of the probability of having the disease. The English phrasing of those two might carry different connotations, but the mathematical logic is the same.
