 |
Decision Tree Modeling
Duration: 2.0 days
Audience
This Level IV course is designed for predictive modelers and data analysts who want to build decision trees using SAS Enterprise Miner software.
Course Description
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.
Prerequisites
Before attending this course, you should
- have an understanding of basic statistical concepts. You can gain this knowledge from the Statistics I: Introduction to ANOVA, Regression, and Logistic Regression course.
- be familiar with SAS Enterprise Miner software. You can gain this knowledge from the Predictive Modeling Using SAS Enterprise Miner 5.1 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
Forests
Software Addressed
This course addresses the following software product(s): SAS Enterprise Miner.
Course Materials
You receive Decision Tree Modeling Course Notes.
|
