HPSPLIT Procedure
The HPSPLIT procedure is a highperformance procedure that builds treebased statistical models for classification and regression. The procedure constructs two types of decision trees: classification trees for modeling categorical responses, and regression trees for modeling continuous responses. The following are highlights of the HPSPLIT procedure's features:
 methods of splitting nodes include criteria based on impurity (entropy, Gini index, residual sum of squares) and criteria based on statistical tests (chisquare, F test, CHAID, FastCHAID)
 computes costcomplexity, C4.5, and reducederror methods of pruning trees
 supports cross validation and validation data for selecting the best subtree
 handles missing values by various methods, including surrogate rules

 tree diagrams, plots for costcomplexity analysis, and plots of ROC curves
 statistics for assessing model fit, including modelbased (resubstitution) statistics and cross validation statistics
 measures of variable importance
 SAS DATA step code for scoring new data

For further details see the SAS/STAT User's Guide: The HPSPLIT Procedure ( PDF  HTML )
Examples