Let's say I have m training examples with n continuous explanatory variables x1, x2,..., xn and a label y. I'm doing a linear regression.
Is there a way to rank what combinations of explanatory variables are actually the most useful to "predict" y ?
For instance, is there an algorithm to tell me what are the best 2 explanatory variables if I only want to use 2 of them? Or to tell me out of the 2^n possible selections of explanatory variables, which ones are the best ? and which explanatory variables are useless / redudant ?