Society of Actuaries (SOA) PA Practice Exam

What does high variance in a model indicate?

1. The model is too simple to capture the data's signal

2. The model accurately predicts outcomes on unseen data

3. The model is overfitting to the training data

4. The model is unbiased and performs well

The correct answer is: The model is overfitting to the training data

High variance in a model indicates that the model is overly complex and is fitting too closely to the training data, capturing noise along with the underlying pattern. This phenomenon, known as overfitting, means that while the model may perform very well on the training dataset, it struggles to generalize to new or unseen data. When high variance is present, the model demonstrates significant sensitivity to the fluctuations in the training set; as a result, it results in poor predictive performance outside of that dataset. In practical terms, this might mean the model makes predictions that vary widely based on small changes in input data, instead of producing stable and reliable outcomes. A model with high variance suggests that it captures the idiosyncrasies of the training data rather than the true relationships that would apply to a broader range of data points. This emphasizes the importance of balancing model complexity with the ability to generalize effectively, a crucial concept in machine learning and statistical modeling.