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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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[–] fordat1@alien.top 1 points 1 year ago

How often is that being applied equally irrespective of submitters reputation. If only reviewers reviewing certain submissions apply it that seems unfair and seems to be the case where the grad student making their first submission at some no name school with the smallest compute budget is getting that acceptance criteria