Society of Actuaries (SOA) PA Practice Exam 2025 – 400 Free Practice Questions to Pass the Exam

In predictive modeling, factor levels with low exposure can introduce excessive noise compared to the useful signal that helps in making predictions. When certain factor levels are underrepresented in the data, their impact on the outcome can be erratic and not reflective of true patterns. This can lead to unstable estimates of the relationships between predictors and the response variable, making it difficult to distinguish meaningful effects from random fluctuations.

Low exposure means fewer data points are available for those particular levels, which can skew the model’s understanding of their significance. As a result, any predictions or inferences drawn from these low-exposure levels may be unreliable, contributing more randomness and noise rather than aiding clear decision-making.

The other concerns regarding low exposure, such as meaningful predictors and the risk of overfitting, are indeed valid; however, the main issue highlighted in this context relates specifically to the noise introduced, which complicates the modeling process and affects overall model performance.