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So, I understand why simple linear or logistic regression will have infinite solutions in this case (good answers here and here). But while LASSO will only select n features, Elastic net does not have this limitation. This answer explains how regularization limits the potential solutions to a problem so that building a model can be possible. Is the same concept true of Elastic Net? If regularization limits the possible solutions, then how is the "final" solution chosen from that space?

kjetil b halvorsen
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Aidan Winters
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    Have a look at https://web.stanford.edu/~hastie/TALKS/enet_talk.pdf – Gabriel Romon May 21 '19 at 19:33
  • @GabrielRomon I looked through this but perhaps the section I need went over my head... where in here do they address the problem of selecting more predictors than observations? – Aidan Winters Jun 11 '19 at 15:36
  • A lot is said on page 9. For the details you definitely want to check [the original paper](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.4696). If something in the paper is unclear, don't hesitate to ask. – Gabriel Romon Jun 11 '19 at 16:17

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