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[D] Why are ML model outputs not tested regarding statistical significance? (alien.top)

submitted 1 year ago by Tigmib@alien.top to c/machinelearning@academy.garden

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Often when I read ML papers the authors compare their results against a benchmark (e.g. using RMSE, accuracy, ...) and say "our results improved with our new method by X%". Nobody makes a significance test if the new method Y outperforms benchmark Z. Is there a reason why? Especially when you break your results down e.g. to the anaylsis of certain classes in object classification this seems important for me. Or do I overlook something?

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[–] kazza789@alien.top 1 points 1 year ago

One big reason for this is that there is a difference between prediction and inference. Most machine learning papers are not testing a hypothesis.

That said - ML definitely does get applied to inference as well, but in those cases the lack of p-values is often one of the lesser complaints.