Society of Actuaries PA Practice Exam Study Guide

After determining the optimal complexity parameter (cp) value in decision trees, a common approach is to build a new tree from scratch. This process allows for the tree to be recalibrated with the new cp value, which serves to prune the tree effectively, balancing bias and variance in the modeling process.

The cp value is crucial as it directly influences the size of the decision tree. By finding the optimal cp, one identifies the threshold where the tree model achieves optimal predictive performance without overfitting the training data. Rebuilding the tree from scratch ensures that the new tree structure adheres to this revised complexity, potentially offering better generalization to unseen data.

The other options do not effectively utilize the cp value. For instance, increasing the complexity of the model contradicts the purpose of finding the optimal cp, which is to simplify the model to enhance predictive performance. Ignoring the cp value and retaining the existing tree would go against the analysis conducted to determine that value, thereby missing out on the potential improvements. Lastly, eliminating all existing splits without review would result in a loss of valuable insights and structure gained from the data, which is not a strategic approach after adjusting the cp.