Society of Actuaries (SOA) PA Practice Exam

Which limitation is associated with feature selection through regularization?

1. It is an easily interpretable technique

2. It is dependent on the model used

3. It guarantees cross-validation success

4. It always retains all variables in the model

The correct answer is: It is dependent on the model used

Feature selection through regularization is a process where the introduction of penalties on the size of coefficients in a model encourages sparsity, meaning it may push some coefficients to zero. The dependence on the model used refers to the fact that different types of regularization, such as LASSO (which can set some coefficients to exactly zero) or Ridge (which shrinks coefficients but does not set them to zero), will yield different sets of selected features. Therefore, the outcome of feature selection cannot be universally applied across all contexts but rather is influenced by the specific regularization technique chosen and the characteristics of the model being used. Each regularization method addresses feature selection differently, thus emphasizing the importance of the model in determining the final set of selected features. Other options do not accurately capture the limitations associated with regularization in the context of feature selection. Regularization methods can vary in their interpretability depending on the model and the approach used. They do not inherently guarantee cross-validation success, as model performance can still vary with different datasets and splits. Furthermore, regularization methods do not always retain all variables in the model, as they can effectively reduce the number of features by zeroing out the coefficients of certain variables.