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

In the context of decision trees, the abbreviation CP stands for Complexity Parameter. The Complexity Parameter is a critical concept used in the pruning process of decision trees. Pruning is a technique that helps to simplify the model by removing sections of the tree that provide little predictive power, thereby reducing overfitting.

The Complexity Parameter serves as a tuning parameter that balances the trade-off between the accuracy of the tree on training data and its simplicity. By adjusting the CP, practitioners can control how much the tree is pruned, with lower values resulting in a more complex model that captures more data variability, and higher values leading to a simpler model that may generalize better to unseen data.

This concept is particularly important in machine learning applications where decision trees are used, as it directly impacts model performance and interpretability. Understanding the role of the Complexity Parameter is essential for effectively building and deploying decision tree models.