Say I want to predict the cancer rate(regression)/predict the whether a person has cancer or not(classification). The data intrinsically has few cancer patients/low cancer rate, say 1/200. And the data set is good and large enough, say more than 100,000.
Now the question is: should I use certain sampling strategy to balance the data before I apply any regression/classification algorithm?
From my perspective, the reason we need to balance the data is because the data we get doesn't follow the natural distribution, it's bad, like a 10/90 male/female. But right now, we have a good data which follows the natural distribution, should we balance the data?
I'm also wondering if things are different for classification versus regression. Though low cancer rate, is it still okay to do the regression without sampling?
Any high-level/detailed ideas are appreciated:)