Society of Actuaries (SOA) PA Practice Exam

Which type of variables can LASSO regularization be applied to after processing?

1. Only numerical variables

2. Only categorical variables

3. Both numerical and categorical variables

4. Neither numerical nor categorical variables

The correct answer is: Both numerical and categorical variables

LASSO (Least Absolute Shrinkage and Selection Operator) regularization is a technique used in regression analysis to enhance prediction accuracy and interpretability by selecting variables and reducing the complexity of the model. After processing, LASSO can indeed be applied to both numerical and categorical variables. For numerical variables, LASSO directly applies its regularization procedure as it deals with continuous data. The regularization process penalizes the absolute size of the coefficients in linear regression, effectively driving some of them to zero, which allows for variable selection in models. For categorical variables, they are typically converted into numerical representations using techniques such as one-hot encoding. This process transforms categorical data into a format that can be used in machine learning algorithms, including LASSO. After encoding, each category becomes a separate binary variable (0 or 1), and LASSO can then be applied to these numerical representations just as it would with numerical variables. Thus, the ability to effectively handle both types of variables after appropriate processing is what makes LASSO a versatile tool in regression analysis.