C&R Tree Overview
The Classification and Regression (C&R) Tree node features tree-based classification and prediction methods. Like C5.0, these methods use recursive partitioning to split the training records into segments with similar output field values. The C&R Tree node starts by examining the input fields to find the best split, measured by the reduction in an impurity index that results from the split. The split defines two subgroups, each of which is subsequently split into two more subgroups, and so on, until one of the stopping criteria is triggered. All splits are binary (only two subgroups).
C&R Trees give you the option to first grow the tree and then prune based on a cost-complexity
algorithm that adjusts the risk estimate based on the number of terminal nodes. This method, which enables the tree to grow large before pruning based on more complex criteria, may result in smaller trees with better cross-validation properties. Increasing the number of terminal nodes generally reduces the risk for the current (training) data, but the actual risk may be higher when the model is generalized to unseen data. In an extreme case, suppose you have a separate terminal node for each record in the training set. The risk estimate would be 0%, since every record falls into its own node, but the risk of misclassification for unseen (testing) data would almost certainly be greater than 0. The cost-complexity measure attempts to compensate for this.
C&R Tree models are probably the most popular choice for single-tree models, due to their excellent performance relative to similar models. They are quite robust in the presence of problems such as missing data and large numbers of fields. They usually do not require long training times to estimate. In addition, C&R Tree models tend to be easier to understand than some other model types–the rules derived from the model have a very straightforward interpretation. Unlike C5.0, C&R Tree can accommodate continuous as well as categorical output fields.