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

submitted 10 months 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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[–] chief167@alien.top 1 points 10 months ago

a combination of different factors:

it is not thought in most self-educated programs.
therefore most actually don't know that 1) it exists 2) how to do it 3) how to do power calculations
since most don't know it, there is no demand for it
costs compute time and resources, as well as human time, so it's skipped if nobody asks for it
there is no standardized approach for ML models. Do you vary only the training, how to partition your dataset? there is no sklearn prebuilt stuff either