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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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[–] SMFet@alien.top 1 points 1 year ago (2 children)

Editor at AI journal and reviewer for a couple of the big conferences. I always ask for statistical significance as the "Rube Goldberg papers" as I call them are SO so common. Papers that complicate things to oblivion without any real gain.

At the very least, a bootstrap of the test results would give you some idea of the potential confidence interval of your test performance.

[–] fordat1@alien.top 1 points 1 year ago

How often is that being applied equally irrespective of submitters reputation. If only reviewers reviewing certain submissions apply it that seems unfair and seems to be the case where the grad student making their first submission at some no name school with the smallest compute budget is getting that acceptance criteria

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

Lol, I hate reviewing those papers. "Interpretable quantized meta pyramidal cnn-vit-lstm with GAN based data augmentation and yyyy-aware loss for XXXX" no GitHub though...