Machine Learning

1 readers

1 users here now

Community Rules:

Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.

founded 11 months ago

MODERATORS

communick@academy.garden

[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

57 comments fedilink hide all child comments

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?

you are viewing a single comment's thread
view the rest of the comments

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

Three main reasons. 1) it's expensive to run multiple experiments, 2) it's nonsensical to split away more data when nobody else is subject to the same constraint 3) if the final artifact is the model and not the method, then in many cases it doesn't matter very much