The HPSPLIT procedure supports the MODEL and CLASS statements, making its syntax comparable to that of other SAS/STAT modeling procedures.
Cost-complexity pruning is supported, with k-fold cross validation as the default method of selecting the penalty parameter.
ODS tables provide confusion matrices for training and validation data; fit statistics for the selected tree; and information about cross validation, nodes, and variable importance.
You can generate tree plots, cross validation plots, and ROC curves.
You can save observationwise results, including node and leaf assignments, predicted levels, posterior probabilities for classification trees, and predicted response values for regression trees in an output data set.