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

Mostly because it's impractical, sometimes because they are lazy or it's simply not statistically significant.

If you train a very large NN it's often to expensive to do it several times. And on very large validation sets you really get significant results pretty fast, so there is not really a need for it. However, I agree that some minor permutations of the NN architecture is often just noise and groups publish it for the sake of publishing.