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The main difference is not in techniques, but in goals. The primary goal of statistics is to help a human make an informed decision by quantifying uncertainty.
by RcouF1uZ4gsC 7y ago
The main difference is not in techniques, but in goals. The primary goal of statistics is to help a human make an informed decision by quantifying uncertainty. That quantification of uncertainty can be used to explain to other humans why the decision was made.
The primary goal of machine learning is to help a machine make better decisions. As long as it gets the "right" answer, the explanations to humans are not as important.
- 6gvONxR4sf7o 7y agoI think that's close, but disagree with the uncertainty quantification bit. Often in both fields, you'll have a proof that "errors" converge at some asymptotic rate. Often in both fields, when you apply that tool to a finite data set, you don't know the error magnitude in this instance. I don't mean exact error, in the sense of 'if I knew the error, I could correct for it.' I mean you know big-O notation error, but not the constant factors. I do like the rest of the point you make, and it seems to match OP. Inference vs otherwise. Helping people learn/act/decide based on data data vs helping machines learn/act/decide based on data.