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Decision Tree Modeling
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Duration: 2.0 days CEUs: 1.2 | ||
| (This course is not currently scheduled.) |
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
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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 or Applied Analytics Using SAS Enterprise Miner 5 course.
Course Contents
Tree-Structured Models
- classification trees
- regression trees
- binary and multiway splits
- splitting criteria
- missing values
- p-value adjustments
- profit/loss considerations
- class probability trees
- cross-validation
- bagging
- arcing
Software
This course addresses SAS Enterprise Miner.
Course Materials
Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.
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Available for on-site training or can be scheduled at any SAS training facility
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