Society of Actuaries (SOA) PA Practice Exam

What are the control parameters typically associated with decision trees?

1. cp, minbucket, learning rate, maxdepth

2. cp, processing time, maxdepth, error rate

3. cp, minbucket, maxdepth, minsplit

4. minsplit, growth factor, cp, threshold

The correct answer is: cp, minbucket, maxdepth, minsplit

The control parameters typically associated with decision trees include the complexity parameter (cp), minbucket, maxdepth, and minsplit. The complexity parameter (cp) is crucial because it helps prevent overfitting by imposing a penalty on the number of splits in the tree. By tuning cp, you can effectively balance model complexity and predictive accuracy. Minbucket defines the minimum number of observations that a terminal node must have, which serves to ensure that the splits create sufficient data in each leaf to make reliable predictions. Maxdepth sets the maximum number of levels in the tree. Limiting the depth of the tree is important to prevent overfitting, as deeper trees can capture more noise within the data, rather than the underlying patterns. Minsplit refers to the minimum number of observations required to split a node. This parameter helps in controlling the growth of the tree and ensuring that splits are made only when there is a sufficient quantity of data to justify the branching. These parameters together provide a robust framework for optimizing the decision tree's structure and performance, enabling effective data analysis while minimizing the risk of overfitting.