Decision Tree Modeling

Duration: 2.0 days
Course fee: $1,360
EPTO units: 2
CEUs: 1.2

This outline is provisional and subject to change.

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

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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

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

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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Are there additional topics you'd like for this course to address? Would you like for this course to be offered at another training facility? Let us know by adding to our Interest List.

Course fee and EPTO units will differ for on-site training.

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