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The HPSPLIT Procedure

Overview
PROC HPSPLIT Features
Getting Started
Syntax
PROC HPSPLIT Statement CLASS Statement CODE Statement GROW Statement ID Statement MODEL Statement OUTPUT Statement PARTITION Statement PERFORMANCE Statement PRUNE Statement RULES Statement
Details
Building a Decision Tree Splitting Criteria Splitting Strategy Pruning Memory Considerations Primary and Surrogate Splitting Rules Handling Missing Values Unknown Values of Categorical Predictors Scoring Measures of Model Fit Variable Importance ODS Table Names ODS Graphics SAS Enterprise Miner Syntax and Notes
Examples
Building a Classification Tree for a Binary Outcome Cost-Complexity Pruning with Cross Validation Creating a Regression Tree Creating a Binary Classification Tree with Validation Data Assessing Variable Importance Applying Breiman’s 1-SE Rule with Misclassification Rate
References

Examples: HPSPLIT Procedure

Subsections:

16.1 Building a Classification Tree for a Binary Outcome
16.2 Cost-Complexity Pruning with Cross Validation
16.3 Creating a Regression Tree
16.4 Creating a Binary Classification Tree with Validation Data
16.5 Assessing Variable Importance
16.6 Applying Breiman’s 1-SE Rule with Misclassification Rate

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