Uploaded September 2018 | Updated September 2026, 1 week ago

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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
youtu.be/EuBBz3bI-aA

Linear Models Part 1: Linear Regression
youtu.be/nk2CQITm_eo

Linear Models Part 1.5: Multiple Regression
youtu.be/zITIFTsivN8

Linear Models Part 2: t-Tests and ANOVA
youtu.be/NF5_btOaCig

Linear Models Part 3: Design Matrices
youtu.be/2UYx-qjJGSs

Cross Validation:
youtu.be/fSytzGwwBVw

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statquest.org/video-index

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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