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

Three main reasons. 1) it's expensive to run multiple experiments, 2) it's nonsensical to split away more data when nobody else is subject to the same constraint 3) if the final artifact is the model and not the method, then in many cases it doesn't matter very much