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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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[–] neo_255_0_0@alien.top 1 points 1 year ago

You'd be surprised to know that most academia is so focused on publishing that the rigor is not even a priority. Forget reproducibility. This is in part because repeated experiments would indeed require more time and resources both of which are a constraint.

That is why the most good tech which can be validated is produced by the industry.