Decision Tree Modeling

Course code: DMDT42

This course includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees. In addition, this course examines many of the auxiliary uses of trees such as exploratory data analysis, dimension reduction, and missing value imputation.

1 200 EUR

1 452 EUR including VAT

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Course dates
Target group
Course structure
Prerequisites

Course dates

Starting date: Upon request

Type: Upon request

Course duration: 14 hours

Language: en

Price without VAT: 1 200 EUR

	Starting date	Place	Type	Course duration	Language	Price without VAT
	Upon request		Upon request	14 hours	en	1 200 EUR	Register
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Target group

Predictive modelers and data analysts who want to build decision trees using SAS Enterprise Miner software

Course structure

Tree-Structured Models

classification trees
regression trees

Recursive Partitioning

binary and multiway splits
splitting criteria
missing values

Pruning

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

Auxiliary Uses of Trees

data exploration
dimension reduction
imputation

Ensembles of Trees

bagging
boosting
gradient boosting
random forests

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 course.