Ridge Regression in R

Ridge Regression in R

Key Points: Ridge regression is a statistical technique used in regression analysis to handle multicollinearity, where predictor variables are highly correlated. The mtcars data set in R provides information on car models, including characteristics and performance metrics, making it suitable for demonstrating ridge regression techniques. Implementing ridge regression in R involves using the "glmnet" package, which offers functions for fitting regularized regression models. By incorporating a ridge penalty term, ridge regression shrinks the regression coefficients towards zero, resulting in more stable and reliable estimates compared to traditional linear regression. The optimal value for the ridge parameter (lambda) can be determined using cross-validation techniques, such as the cv.glmnet() function, to find the lambda that minimizes the mean squared error or another suitable criterion. Ridge regression is a widely used statistical technique for regression analysis that can effec…