Lasso Regression in R [Update 2024]

Lasso Regression in R [Update 2024]

Key Points Lasso regression is a type of linear regression that adds a penalty term to the loss function that is proportional to the sum of the absolute values of the coefficients. This penalty term is also known as the L1 norm of the coefficients. Lasso regression can perform variable selection by shrinking some of the coefficients to exactly zero, thus removing some predictors from the model. This can help reduce overfitting and improve interpretability. Lasso regression can handle multicollinearity by assigning similar coefficients to correlated predictors, thus reducing their individual influence on the model. To perform lasso regression in R, we can use the glmnet package, which provides functions for fitting generalized linear models with L1 and L2 regularization. The main function is glmnet, which takes a matrix of predictor values (x) and a vector of target values (y) as arguments, and returns an object of class “glmnet”, which contains information about the fitted model. We can s…