Society of Actuaries (SOA) PA Practice Exam

Which of the following is a benefit of using Lasso Regression over traditional selection methods?

1. It guarantees optimal model accuracy

2. It can automatically select significant variables

3. It works only for linear relationships

4. It never reduces coefficients to zero

The correct answer is: It can automatically select significant variables

Lasso Regression, short for Least Absolute Shrinkage and Selection Operator, offers a key benefit in that it can automatically select significant variables during the model fitting process. What makes Lasso particularly valuable is its ability to impose a penalty on the size of the coefficients, which can lead to some coefficients being shrunk exactly to zero. This effectively means that Lasso not only performs variable selection but also helps in handling multicollinearity and reduces overfitting by simplifying the model, making it easier to interpret. This automatic selection capability is a significant advantage over traditional selection methods that may require manual intervention or criteria that do not adapt well to the data at hand. Traditional methods, such as stepwise regression, rely on predefined statistical tests that may not always yield the best subset of variables. Lasso Regression is strong in identifying and retaining only the most relevant predictors while discarding others, thus enhancing model performance and interpretability. This aspect of Lasso aligns perfectly with modern data analysis needs, where the importance of feature selection and model simplicity is often emphasized.