Society of Actuaries (SOA) PA Practice Exam

Which of the following is a parameter of Random Forest models?

1. Learning Rate

2. Number of Trees

3. Regularization Parameter

4. Gradient Boosting Steps

The correct answer is: Number of Trees

The correct response is focused on the "Number of Trees" as a key parameter in Random Forest models. Random Forest is an ensemble learning method that relies on constructing a multitude of decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees. One of the main parameters that can be adjusted in a Random Forest model is indeed the number of trees to be created. Increasing the number of trees typically improves the model's performance by reducing variance and enhancing accuracy, as the predictions become more stable and reliable through the aggregation of multiple trees. The other choices relate to different modeling techniques. The "Learning Rate" is a parameter associated with algorithms that utilize gradient descent, such as boosting methods, rather than Random Forests. "Regularization Parameter" is typically seen in linear models or other regression techniques, where it helps prevent overfitting by penalizing large coefficients. Finally, "Gradient Boosting Steps" relates specifically to boosting algorithms, which build trees sequentially to correct errors from previous trees, rather than employing the parallel tree-building method utilized in Random Forests. Each of these parameters is significant in their respective models, but the number of trees is specifically relevant to the architecture and operation of Random Forest