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

Random Forests is built on the fundamental process of bagging, which stands for bootstrap aggregating. This technique involves creating multiple independent decision trees that are generated from different subsets of the training data. Each tree is constructed using a random sample drawn with replacement, which means that some observations may be repeated while others may not be included at all. This diversity among trees helps to reduce overfitting and improves the overall predictive performance of the model.

The ensemble of these decision trees leads to a more robust model, as the errors of individual trees tend to cancel each other out when predictions are aggregated (usually through voting or averaging). This process enhances the model's generalization capability on unseen data.

The other options do not capture the essential mechanism of Random Forests. For example, relying on a single decision tree framework would not take advantage of the ensemble approach that makes Random Forests effective. Similarly, solely focusing on a linear regression approach does not apply, as Random Forests can handle complex non-linear relationships. Finally, the requirement for all data points to be included without exclusion contradicts the principles of bagging, which intentionally uses random sampling to create varied training sets for different trees.