Machine Learning - Regularization Part 1: Ridge (L2) Regression

Ridge Regression is a neat little way to ensure you don't overfit your training data - essentially, you are desensitizing your model to the training data. It can also help you solve unsolvable equations, and if that isn't bad to the bone, I don't know what is. This StatQuest follows up on the StatQuests on: Bias and Variance https://youtu.be/EuBBz3bI-aA Linear Models Part 1: Linear Regression https://youtu.be/nk2CQITm_eo Linear Models Part 1.5: Multiple Regression https://youtu.be/zITIFTsivN8 Linear Models Part 2: t-Tests and ANOVA https://youtu.be/NF5_btOaCig Linear Models Part 3: Design Matrices https://youtu.be/2UYx-qjJGSs Cross Validation: https://youtu.be/fSytzGwwBVw 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 0:00 Awesome song and introduction 1:25 Ridge Regression main ideas 4:15 Ridge Regression details 10:21 Ridge Regression for discrete variables 13:24 Ridge Regression for Logistic Regression 14:12 Ridge Regression for fancy models 15:34 Ridge Regression when you don't have much data 19:15 Summary of concepts Correction: 13:39 I meant to say "Negative Log-Likelihood" instead of "Likelihood". #statquest #regularization