Regularization Part 1: Ridge (L2) Regression скачать в хорошем качестве

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    • Machine Learning Fundamentals: Bias and Va...   Linear Models Part 1: Linear Regression    • Linear Regression, Clearly Explained!!!   Linear Models Part 1.5: Multiple Regression    • Multiple Regression, Clearly Explained!!!   Linear Models Part 2: t-Tests and ANOVA    • Using Linear Models for t-tests and ANOVA,...   Linear Models Part 3: Design Matrices    • StatQuest: Linear Models Pt.3 - Design Mat...   Cross Validation:    • Machine Learning Fundamentals: Cross Valid...   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:   / statquest   ...or... YouTube Membership:    / @statquest   ...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:   / 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