Machine Learning - 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... https://youtu.be/Q81RR3yKn30 ...and the StatQuest on Lasso Regression.... https://youtu.be/NGf0voTMlcs Also, here are some references that helped me put this video together: The original manuscript on Elastic-Net Regression: https://web.stanford.edu/~hastie/Papers/B67.2%20%282005%29%20301-320%20Zou%20&%20Hastie.pdf 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/LASSO_Ridge_Elastic_Net_-_Examples.html For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...buying one of my books, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... https://statquest.org/statquest-store/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer #statquest #regularization