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in R, once I call https://www.rdocumentation.org/packages/glmnet/versions/4.1-2/topics/cv.glmnet with alpha = 0, I will magically get a set of coefficients from ridge regression, without having to specify anything about the penalty coefficient.

How does glmnet determine the penalty for ridge regression?

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The cv.glmnet function uses k-fold cross-validation to estimate an optimal penalty term. The default for this software is to use 10 folds. So, the software fits many ridge regressions on a grid of different penalty values and then chooses the value of the penalty parameter that minimizes estimated out-of-sample prediction error, using cross-validation to estimate the out-of-sample prediction error for each choice of the parameter value.

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  • $\begingroup$ Is there something going on with 1se, or is that a different package? $\endgroup$
    – Dave
    Commented Sep 14, 2021 at 13:33
  • $\begingroup$ The 1se choice of lambda can be calculated in the same package. It is a heuristic that uses a slightly different criterion to choose the "best" lambda, see: stats.stackexchange.com/questions/138569/… $\endgroup$
    – frelk
    Commented Sep 14, 2021 at 13:48

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