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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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[–] Zestyclose_Speed3349@alien.top 1 points 1 year ago (1 children)

It depends on the field. In DL repeating the training procedure N times may be very costly which is unfortunate. In RL it's common to repeat the experiments 25-100 times and report standard error.

[–] Consistent_Walrus_23@alien.top 1 points 1 year ago

Agreed, RL is extremely stochastic and the outcomes can be pretty random due to monte carlo sampling.