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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 • Machine Learning Fundamentals: Bias a... Linear Models Part 1: Linear Regression • Linear Regression, Clearly Explained!!! Linear Models Part 1.5: Multiple Regression • Multiple Regression, Clearly Explaine... Linear Models Part 2: t-Tests and ANOVA • Using Linear Models for t-tests and A... Linear Models Part 3: Design Matrices • StatQuest: Linear Models Pt.3 - Desig... Cross Validation: • Machine Learning Fundamentals: Cross ... 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... Buying The StatQuest Illustrated Guide to Machine Learning!!! PDF - https://statquest.gumroad.com/l/wvtmc Paperback - https://www.amazon.com/dp/B09ZCKR4H6 Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC Patreon: / statquest ...or... YouTube Membership: / @statquest ...a cool StatQuest t-shirt or sweatshirt: https://shop.spreadshirt.com/statques... ...buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...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: / 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