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 1 year ago

MODERATORS

communick@academy.garden

[D] Why are ML model outputs not tested regarding statistical significance? (alien.top)

submitted 11 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

[–] Crimsoneer@alien.top 1 points 11 months ago (2 children)

Statistical significance was designed in an age when you had 300 observations to represent a population of hundreds of thousands. It is far less meaningful when you move into the big data space.

[–] bankimu@alien.top 1 points 11 months ago

Wow that was a lot of bollocks in a small space.

[–] kniglas@alien.top 1 points 11 months ago

I second that. Additionally I would say, statistics are designed for hypothesis testing. You have an idea, make a priori assumptions (! e.g. which variables to look at!), collect sample data and then want to know if the findings can be generalized to the entire population (or: everything/everyone in the world). The underlying idea is to better understand the workings of the world.

My own experience, as a pretty traditionally trained researcher (i.e. knowing a bit about statistics) leading a group of data scientist is that the goal is really different. My data scientist try to build a modlę that works, that is the primary goal. I am trying to understand why something is that way, even if that means that my (a priori) build model doesn't work.

The boarder between "traditional stats" and ML are very fluid. ML uses a lot of stats, and hypothesis testing research uses a lot ML these days. Just the underlying motivation might be slightly different.