Society of Actuaries (SOA) PA Practice Exam

Which of the following is a step to validate a GLM regression model?

1. Evaluate the predictions against a random sample

2. Assess the RMSE against an OLS model

3. Choose the model with the least complexity

4. Visualize the training data with a histogram

The correct answer is: Assess the RMSE against an OLS model

The correct choice involves assessing the Root Mean Square Error (RMSE) against an Ordinary Least Squares (OLS) model, which is a crucial step in validating a Generalized Linear Model (GLM) regression model. RMSE is a widely used metric for evaluating the performance of predictive models, as it quantifies the difference between predicted values and actual observations. By comparing the RMSE of the GLM with that of a traditional OLS model, you can determine if the GLM provides a better fit to the data. This comparison helps ensure that the GLM is not only well-calibrated but also offers improvements in prediction accuracy relative to a simpler linear model. Such validation is essential in the model selection process, as it provides insights into whether the added complexity of a GLM is justified by a significant gain in predictive power. Other options, while they might seem relevant in different contexts, do not directly address the specific validation of a GLM in the same way. For instance, evaluating predictions against a random sample or visualizing data may assist in understanding model behavior or data distribution, but they do not provide a clear quantitative measure like RMSE for assessing model performance in comparison to OLS. Choosing a model with the least complexity does not inherently