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What is L0 Regularization in Linear Regression about?

This lecture discusses linear regression with regularization. It begins with a recap of linear regression and how overfitting can occur when there are too many parameters relative to the training data. Regularization is introduced as a way to avoid overfitting by adding a penalty term for the size of the weights. Specifically, L2 regularization adds a penalty term that is proportional to the square of the weights. This results in a closed-form solution for the regularized linear regression. L1 and L0 regula

Author

Tuytm2

Language

EN