Society of Actuaries (SOA) PA Practice Exam

What does Feature Importance analyze in a model?

1. The number of features used

2. The accuracy of predictions

3. The contribution of each feature

4. The computational efficiency of the model

The correct answer is: The contribution of each feature

Feature Importance assesses the contribution of each feature in a predictive modeling context. It helps to quantify how much each variable contributes to the predictions made by the model. By understanding which features are most important, data scientists and stakeholders can interpret the model better, gain insights into the underlying relationships in the data, and potentially simplify the model by focusing on the most relevant variables. The concept is crucial in feature selection processes, where one seeks to identify the most influential features to improve model performance while potentially reducing complexity. Analyzing feature importance can also assist in model diagnostics and provide guidance on which features may need further investigation or modification. In contrast, while the number of features used is important for model complexity, it doesn't directly reflect the impact of each feature. The accuracy of predictions relates to how well the model performs overall, rather than how individual features contribute to that performance. Similarly, computational efficiency pertains to how resource-intensive the model is during training and prediction, which is not directly linked to individual feature contributions.