- [[L1 regularization]], [[elastic net regularization]], [[regularization]], [[regularized logistic regression]], [[L1 vs L2 regularization]], [[coordinate descent]] # Idea For [[linear regression]] with [[L2 regularization]], it's called **Ridge**. But for other models like [[logistic regression]], we simply say L2. Ridge regression is a special case of [[L2 regularization|Tikhonov regularization]]. Unlike L1, L2 doesn't perform feature selection. Note that by default, [[scikit-learn]]'s [[logistic regression]] uses [[L2 regularization]]. Smaller `C` parameter values indicate greater regularization. L2 regularization adds the squared of the coefficients, sums them, and adds them to the error term. ![[s20220620_123202.png]] ![[Pasted image 20210125203758.png]] # References - https://campus.datacamp.com/courses/linear-classifiers-in-python/logistic-regression-3?ex=1 - [udacity](https://www.youtube.com/watch?v=PyFNIcsNma0&feature=emb_logo)