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No one uses or needs ML for overfitting 4 variables. You can do that with regular statistics just fine. And how you interpret ML results is just as fraught with
by Cacti 8y ago
No one uses or needs ML for overfitting 4 variables. You can do that with regular statistics just fine. And how you interpret ML results is just as fraught with error as any statistical argument—just because the technique gives you some result doesn’t mean it’s explanatory, that is science 101.
- Jeff_Brown 8y ago> 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.