Talk:Ridge regression

The contents of Tikhonov regularization was merged into Ridge regression on 17 October 2022. The former page's history now serves to provide attribution for that content in the latter page, and it must not be deleted as long as the latter page exists. For the discussion at that location, see its talk page.

Merging that way is not advised, they are overlapping methods but in different theoretical frameworks, and the Bayesian is more general. -Anon

Fold into Tikhanov regression?

Ridge regression is Tikhanov regression. And that areticle is much more detailed. I propose we merge the two articles, basically delete this one and point to the other David (talk) 01:42, 22 January 2022 (UTC)[reply]

Yeah, it definitely should be; this article is also worse than the one on Tikhonov regularization. For instance, this article claims that ridge regression is used to deal with multicollinearity, when in reality there is no need for the variables to be correlated. Ridge regression just regularizes coefficients towards 0 to improve out-of-sample performance. Closed Limelike Curves (talk) 00:49, 15 May 2022 (UTC)[reply][reply]

Explain the name

The article left me confused about the name of the regression. Was the name originally 'RIDGE' and was thus originally an abbreviation? Did it later change to 'Ridge', maybe due to the abbreviation being clumsy in hindsight? Or is this referring to something within the mathematical method?

bilderbikkel 11:18, 6 May 2022 (UTC)[reply]

Applications

The method (Tikhonov regularization) is applied in several fields of applied physics like,

Geophysics (seismic studies for example)
Atmospheric inverse problem, in general it is an important method in remote sensing
Medicine in studies using functional magnetic resonance imaging.
Deep learning regularization techniques are also using this method (for genomic data for instance)

I would suggest to add this kind of information to the page. AyubuZimbale (talk) 07:31, 4 November 2024 (UTC)[reply]