Society of Actuaries (SOA) PA Practice Exam 2025 – Comprehensive All-in-One Guide to Mastering Your Exam Success!

Lasso Regression is best described as a method that performs variable selection and regularization. This technique is particularly useful in situations where you have a dataset with many predictors, as it helps to prevent overfitting by applying a penalty to the absolute size of the coefficients associated with the predictors.

The key characteristic of Lasso is its ability to shrink some coefficients to exactly zero, effectively excluding those variables from the model. This property aids in simplifying the model and identifying the most significant predictors, thus enhancing interpretability and performance on unseen data.

In contrast, increasing complexity, retaining all variables, or being non-penalized does not accurately characterize Lasso Regression. These alternative approaches could lead to overfitting or fail to focus on the most relevant predictors, which is counter to what Lasso aims to achieve.