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

Learn how to

• 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

Predictive modelers and data analysts who want to build decision trees using SAS Enterprise Miner software

Formats available	Standard Duration
Classroom:	2.0 days
Live Web Classroom:	4 half-day session(s)	System Requirements

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.

Tree-Structured Models

• classification trees
• regression trees

Recursive Partitioning

• binary and multiway splits
• splitting criteria
• missing values

Pruning

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

Auxiliary Uses of Trees

• data exploration
• dimension reduction
• imputation

Ensembles of Trees

• bagging
• boosting
• gradient boosting
• random forests