Society of Actuaries (SOA) PA Practice Exam 2026 – Comprehensive All-in-One Guide to Mastering Your Exam Success!

The maximum depth of any node in a decision tree is governed by a specific parameter that directly sets a limit on how deep the tree can grow as it splits based on input features. In this context, the parameter responsible for this is indeed the maximum depth parameter.

When you specify a maximum depth in a decision tree model, you are restricting the number of levels of splits that can be made in the tree. This has a significant impact on the model's complexity and its ability to overfit or generalize to unseen data. A shallower tree (lower maximum depth) simplifies the model, potentially increasing its ability to generalize, while a deeper tree may capture more detailed patterns in the training data but at the risk of overfitting.

The other parameters serve different purposes: the complexity parameter and minsplit relate to the tree's growth and pruning aspects, while minbucket specifies the minimum number of observations that must be present in a terminal node. These do not set the maximum depth of the tree directly. By effectively managing the maximum depth, one can control the size and complexity of the decision tree for better model performance.