There is a new version of this course. Please see Decision Tree Modeling.

This course covers tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees. In addition, this course discusses many of the auxiliary uses of trees such as exploratory data analysis, dimension reduction, and missing value imputation.

• build tree-structured models including classification trees and regression trees
• use the methodology for growing, pruning, and assessing decision trees
• use decision trees for exploratory data analysis, dimension reduction, and missing value imputation.

Who should attend

Formats available	Standard duration
Classroom:	2.0 days

Before attending this course, you should

• have an understanding of basic statistical concepts. You can gain this knowledge from the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.
• be familiar with SAS Enterprise Miner software. You can gain this knowledge from the Applied Analytics Using SAS Enterprise Miner course.

This course addresses SAS Enterprise Miner software.

• classification trees
• regression trees

• binary and multiway splits
• splitting criteria
• missing values

• p-value adjustments
• profit/loss considerations
• class probability trees
• cross-validation

• data exploration
• dimension reduction
• imputation

• bagging
• arcing
• gradient boosting