Ridge Regression

• L2 regularization (MSE)
• Works by adding this term $\alpha * |coefficients^2|$ in the cost function so we are penalizing the size of the coefficients
  • This will uniformly decrease the coefficients
  • The parameter $\alpha$ will control the shrinkage which makes the coefficients more robust to collinearity
• The alpha term can be found by using cross-validation techniques
  • sklearn.linear_Regression.RidgeCV uses leave-one-out-cross-validation technique to find the alpha that minimizes the cross-validated mean squared error