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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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[–] econ1mods1are1cucks@alien.top 1 points 1 year ago (1 children)

Depends on how big the individual samples are tbh. 1000 samples of 10 people actually sounds like a decent study group

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

I see what you mean. Yeah it shouldn't be by default I don't do statistical significance tests.