8.10 An example: tree pruning (Hitters data)

Results of fitting and pruning a regression tree on the Hitters data using 9 of the features
Randomly divided the data set in half (132 observations in training, 131 observations in the test set)
Built large regression tree on training data and varied \(\alpha\) in Eq. 8.4 to create subtrees with different numbers of terminal nodes
Finally, performed 6-fold cross-validation to estimate the cross-validated MSE of the trees as a function of \(\alpha\)

Training, cross-validation, and test MSE are shown as a function of the number of terminal nodes in the pruned tree. Standard error bands are displayed. The minimum cross-validation error occurs at a tree size of 3.

Figure 8.1: Training, cross-validation, and test MSE are shown as a function of the number of terminal nodes in the pruned tree. Standard error bands are displayed. The minimum cross-validation error occurs at a tree size of 3.