[L1 Regularization] Why LASSO Sets Weights to EXACTLY Zero
Why does LASSO drive some weights all the way to zero, while Ridge usually only shrinks them?
In this video, we break down the intuition behind L1 regularization using geometric visualization and simple examples.
Timestamps
00:00 [Intro] Lp Norm Family
00:50 L1 vs L2 Loss Review - Gaussian error vs Laplace error
01:37 Bayesian View of Regularization - Gaussian prior vs Laplace prior
02:45 Introduction to LASSO - Why L1 regularization matters
03:07 How do we solve LASSO? (Intro to Coordinate Descent)
03:42 The reason we use LASSO
04:21 HOW LASSO Sets Weights to Zero (Intuition)
05:20 When to Use Ridge vs LASSO
06:21 Summary - Key Benefits of LASSO
By the end, you'll clearly understand why LASSO is powerful when many features are irrelevant, and why Ridge works better when most features matter.
Perfect for beginners learning machine learning, linear regression, and regularization.
Coordinate Descent Youtube: • 6. L1 & L2 Regularization
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All animations in this video were made with ManimGL and ManimCE, an open-source animation library built in Python. You can find more information about the project here: https://github.com/3b1b/manim, https://github.com/manimCommunity/manim
manimCE: https://www.manim.community/
#MachineLearning #L2 Regularization#Ridge#Overfitting#manim#MLIntuition