Decision Tree Modeling

Duration: 2.0 days

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.

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

Prerequisites

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 5.3 course.

Course Contents

Tree-Structured Models

• classification trees
• regression trees

Recursive Partitioning

• binary and multiway splits
• splitting criteria
• missing values

Pruning

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

Auxiliary Uses of Trees

• data exploration
• dimension reduction
• imputation

Ensembles of Trees

• bagging
• arcing
• gradiant boosting

Software Addressed

This course addresses the following software product(s): SAS Enterprise Miner.

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