L1 and L2 Regularization Methods – Towards Data Science – Medium

In my last post, I covered the introduction to Regularization in supervised learning models. In this post, let's go over some of the regularization techniques widely used and the key difference between those. A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. The key difference between these two is the penalty term. Ridge regression adds "squared magnitude" of coefficient as penalty term to the loss function.