Regularization Part 3: Elastic Net Regression

Elastic-Net Regression is combines Lasso Regression with Ridge Regression to give you the best of both worlds. It works well when there are lots of useless variables that need to be removed from the equation and it works well when there are lots of useful variables that need to be retained. And it does better than either one when it comes to handling correlated variables. Dang!!!!

NOTE: This StatQuest follows up on the the StatQuest on Ridge Regression...
   • Regularization Part 1: Ridge (L2) Reg...  
...and the StatQuest on Lasso Regression....
   • Regularization Part 2: Lasso (L1) Reg...  

For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/

Also, here are some references that helped me put this video together:
The original manuscript on Elastic-Net Regression: https://web.stanford.edu/~hastie/Pape...

A webpage at North Carolina State University that shows different situations for Ridge, Lasso and Elastic-Net Regression: https://www4.stat.ncsu.edu/~post/josh...

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