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 Linear Models Part 1: Linear Regression Linear Models Part 1.5: Multiple Regression Linear Models Part 2: t-Tests and ANOVA Linear Models Part 3: Design Matrices Cross Validation: For a complete index of all the StatQuest videos, check out: If you’d like to support StatQuest, please consider... Buying The StatQuest Illustrated Guide to Machine Learning!!! PDF - Paperback - Kindle eBook - Patreon: ...or... YouTube Membership: ...a cool StatQuest t-shirt or sweatshirt: ...buying one or two of my songs (or go large and get a whole album!) ...or just donating to StatQuest! Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: 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