The HPSPLIT Procedure

supports interval and nominal inputs

supports nominal targets (decision trees)

supports interval targets (regression trees)

provides the entropy, Gini, FastCHAID, CHAID, information gain ratio (IGR), and chi-square methods for decision tree growth (for nominal targets)

provides the variance, CHAID, and F test methods for regression tree growth (for interval targets)

provides multiple statistical metrics for decision tree pruning

provides C4.5-style decision tree pruning

provides ASE-based regression tree pruning

provides cost-complexity pruning

provides minimum metric subtree selection

partitions the input data set into training and validation sets

provides surrogate rules in addition to popularity, similarity, or a dedicated branch for missing value assignments

saves scored results for the training data

provides predicted levels and posterior probabilities

saves SAS DATA step code, which can be used for scoring new data with the tree model

saves node rules that describe the leaves of the tree

saves statistics and descriptive information for the nodes in the tree

saves the importance of the input variables in creating the pruned decision tree

provides variable importance for the validation set

save statistical metrics for each subtree that is created during growth

save statistical metrics for each subtree that is created during pruning