How I Perform Ridge Regression in R [Update 2024]

How I Perform Ridge Regression in R [Update 2024]

Key points Ridge regression is a method of regularization that can help you deal with multicollinearity, improve the accuracy of your predictions, and reduce the complexity of your model. Ridge regression adds a penalty term to the ordinary least squares objective function, which is proportional to the sum of squared coefficients of the regression model. The penalty term is controlled by a lambda parameter, which determines how much the coefficients are shrunk towards zero. To implement ridge regression in R, you need to use the  glmnet  package, which provides functions for fitting generalized linear models with various types of regularization. To choose the optimal value of lambda, you need to use cross-validation, a technique that splits the data into several subsets and uses some for training and some for testing. Ridge Regression in R: Best Practices & Techniques  Ridge regression is a method of regularization that can help you deal with multicollinearity, improve the accuracy of your…