Decision Tree Modeling – DMDT71
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.
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|2 days - Classroom|
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
Before attending this course, you should:
- have an understanding of basic statistical concepts. You can gain this knowledge from the Introduction to Statistics using SAS: ANOVA, Linear 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.
- classification trees
- regression trees
- binary and multiway splits
- splitting criteria
- missing values
- p-value adjustments
- profit/loss considerations
- class probability trees
Auxiliary Uses of Trees
- data exploration
- dimension reduction
Ensembles of Trees
- gradiant boosting
This course addresses SAS Enterprise Miner software.