Society of Actuaries (SOA) PA Practice Exam 2025 – Comprehensive All-in-One Guide to Mastering Your Exam Success!

The term that describes the sections of a decision tree that are non-critical and can be removed is known as pruning. Pruning is a technique used in decision tree algorithms to enhance the model’s performance by reducing complexity and avoiding overfitting.

When a decision tree is built, it might create many branches based on the training data, leading to a model that fits the noise rather than the underlying pattern. Pruning helps address this issue by removing branches that offer little improvement to the model's predictive power. This results in a simpler, more interpretable model that generalizes better to new data.

In the context of decision trees, pruning can involve removing branches that do not contribute significantly to the accuracy of predictions, thereby streamlining the decision-making process without sacrificing model effectiveness. This is crucial for improving the model's performance on unseen data and enhancing its applicability in real-world scenarios.