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I have a theoretical question. I was reading about ridge regression, lasso and the elastic net, and is very impressed. One thing is not quite clear to me.

I would like to know when should I use each model: Linear regression, ridge, lasso and elastic net. What are the advantages and disadvantages of them all ?

I know that an advantage of the lasso is that it does not only shrinkage but also feature selection, it can force the coefficients to zero. Is there anything else ? Why using ridge then, ever ? And what about elastic net, can it shrink coefficients to zero? why would anyone want to combine lasso with ridge, I mean, lasso also shrinks the coefficients.

I am slightly confused.

user3275222
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  • Some discussion: https://stats.stackexchange.com/questions/184029/what-is-elastic-net-regularization-and-how-does-it-solve-the-drawbacks-of-ridge/184031#184031 – Sycorax May 26 '19 at 17:43
  • I actually can't find a dataset where Lasso or Ridge does significantly better than linear regression. – user3275222 May 27 '19 at 17:03

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