For my particular domain and problem, I have data on the entire population. However, my "event" only occurs in 0.5% of the cases. I want my model to be able to pick up on significant characteristics in the minority class (the "event" class) to better predict in the future, but my understanding, after reading through several papers and a few SAS blog posts today, is that oversampling when you already have the population isn't good practice, as you already have the entire population -- what more could you want?
In the case of logistic regression, oversampling wouldn't affect coefficients (outside of the slope intercept), so I don't see a reason to oversample in the case of that model. But what about for random forests or support vector machines? Would oversampling when I already have the entire population be a good or bad idea?
I guess my core question is: when shouldn't you oversample?