this post was submitted on 25 Nov 2023
1 points (100.0% liked)

Machine Learning

1 readers
1 users here now

Community Rules:

founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
[–] Appropriate_Ant_4629@alien.top 1 points 11 months ago (1 children)

We need a name for the fallacy where people call highly nonlinear algorithms with billions of parameters "just statistics"

Well, thanks to quantum mechanics; pretty much all of existence is probably "just statistics".

as if all they're doing is linear regression.

Well, practically all interesting statistics are NONlinear regressions. Including ML. And your brain. And physics.

[–] KoalaNumber3@alien.top 1 points 11 months ago (1 children)

What a lot of people don’t understand is that linear regression can still handle non-linear relationships.

For a statistician, linear regression just means the coefficients are linear, it doesn’t mean the relationship itself is a straight line.

That’s why linear models are still incredibly powerful and are used so widely across so many fields.

[–] Appropriate_Ant_4629@alien.top 1 points 11 months ago

Yet still limited compared to even not-very-deep NNs. If the user wants to fit a parabola with a linear regression, he pretty much has to manually add a quadratic term himself.

I think they're widely used primarily because they're widely taught in school.