Ridge Regularization (L2 Regularization) › Summary

Ridge regression (L2 regularization) is a technique that prevents overfitting by adding a penalty term proportional to the sum of squared coefficients. Unlike LASSO, Ridge shrinks coefficients toward zero but does not eliminate features entirely, making it well-suited for datasets with multicollinear features where all variables may be relevant. The method provides stable solutions even when the number of features exceeds the number of observations, and its closed-form solution makes it computationally efficient. However, Ridge requires careful feature scaling and parameter tuning to achieve optimal performance. It is particularly valuable in high-dimensional settings where maintaining all features while controlling overfitting is important.