Ace the Society of Actuaries PA Exam 2026 – Power Up Your Professional Path!

The function used to perform Lasso Regression in R is glmnet. This function is specifically designed for fitting generalized linear models with elastic net penalties, which includes Lasso as a special case when the penalty for the L1 norm is applied.

Lasso Regression is popular for model selection and regularization because it can shrink some coefficients to zero, effectively selecting a simpler model that may generalize better to unseen data. The glmnet function efficiently handles large datasets and can fit models to input data structured in a matrix format for ease of computation. It allows users to specify the alpha parameter, where an alpha of 1 corresponds to Lasso Regression.

While cv.glmnet is also related to Lasso Regression, as it performs cross-validation to help determine the best lambda (the penalty term), it does not fit the model directly. Instead, it uses glmnet internally. The lm function fits a linear model without regularization, and stepAIC is used for model selection based on the Akaike Information Criterion, neither of which are appropriate for Lasso Regression.