> No one uses or needs ML for overfitting 4 variables. You can do that with regular statistics just fine. True, but an ML routine can try an approximately infi

> No one uses or needs ML for overfitting 4 variables. You can do that with regular statistics just fine. True, but an ML routine can try an approximately infinitely greater number of models. If I'm using x,y and z to predict w, and I've tried all the linear terms, all the squared terms, all the interactions, all the log terms, and I start throwing in other things, my readers will, rightly, raise an eyebrow. Maybe there's some discontinuity to exploit, but if so, I'll explain it -- a policy change for people age 65 or older, say, or a market that exists in one state and not an adjacent one. The ML, by contrast, can invent the most absurdly jagged multivariate functions imaginable, and we typically* don't even know that it's doing so, let alone why. *as others have written here, we can actually investigate the how (not the why) by inspecting the algorithm -- but the number of papers that do is much smaller than the number that don't.