Society of Actuaries (SOA) PA Practice Exam 2025 – 400 Free Practice Questions to Pass the Exam

The primary purpose of pruning in machine learning, particularly in the context of decision trees, is to reduce the complexity of the final model. Pruning involves removing parts of the tree that do not provide significant power in predicting target outcomes, thereby preventing overfitting. Overfitting occurs when a model learns noise and details from the training data to the extent that it adversely affects the model's performance on new, unseen data. By simplifying the model, pruning helps to ensure that it generalizes better to new data points.

In the context of decision trees, pruning typically means cutting off branches of the tree that contribute little to predictive power. This results in a model that maintains predictive accuracy while being less complex and easier to interpret. Therefore, the focus is on enhancing the model's performance on validation datasets rather than just fitting the training data excessively.

This understanding emphasizes the importance of model simplicity in machine learning and how it can lead to more robust predictions, aligning with best practices in model validation and assessment.