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

Cross-validation is a reasonable alternative, however, it does increase your compute cost 5-10 times, or, more likely, means that you generate 5-10 times smaller model(s) which are worse than you could have made if you'd just made a single one.