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

Pruning a decision tree primarily serves the purpose of reducing overfitting and complexity. As a tree grows, it may become overly complex by capturing noise in the training data, leading to poor performance on unseen data. Pruning involves removing branches or nodes that contribute little to the predictive power of the model, which can enhance its generalizability.

By eliminating unnecessary complexity, pruned trees maintain essential structures that are beneficial for decision-making while discarding overly specific interpretations of the data. This balance helps improve the tree’s performance on test datasets, making it more robust and capable of accurately predicting outcomes in various scenarios.

In contrast, options that suggest increasing complexity or expanding the number of branches do not align with the fundamental purpose of pruning. Additionally, changing the data used for modeling is unrelated to the pruning process, which focuses solely on the structure of the decision tree itself. The goal is to streamline the model without losing important information that aids accurate predictions.