Decision Trees for Business Intelligence and Data Mining_ Using SAS Enterprise Miner [de Ville 2006-10-30](1).pdf

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The correct bibliographic citation for this manual is as follows: deVille, Barry. 2006.
Decision Trees for
®
Business Intelligence and Data Mining: Using SAS Enterprise Miner™.
Cary, NC: SAS Institute Inc.
Decision Trees for Business Intelligence and Data Mining: Using SAS
®
Enterprise Miner™
Copyright © 2006, SAS Institute Inc., Cary, NC, USA
ISBN-13: 978-1-59047-567-6
ISBN-10: 1-59047-567-4
All rights reserved. Produced in the United States of America.
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1st printing, November 2006
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Contents
Preface ................................................................................................ vii
Acknowledgments ...............................................................................xi
Chapter 1 Decision Trees—What Are They? ..................... 1
Introduction ..........................................................................................1
Using Decision Trees with Other Modeling Approaches ...................5
Why Are Decision Trees So Useful? ...................................................8
Level of Measurement .......................................................................11
Chapter 2 Descriptive, Predictive, and Explanatory
Analyses......................................................... 17
Introduction
..................................................................................................
18
The Importance of Showing Context .................................................19
Antecedents ................................................................................21
Intervening Factors .....................................................................22
A Classic Study and Illustration of the Need to
Understand Context ...........................................................................23
The Effect of Context..........................................................................25
How Do Misleading Results Appear? ................................................26
Automatic Interaction Detection ...............................................28
The Role of Validation and Statistics in Growing Decision Trees ....34
The Application of Statistical Knowledge to Growing
Decision Trees ....................................................................................36
Significance Tests.......................................................................36
The Role of Statistics in CHAID..................................................37
Validation to Determine Tree Size and Quality..................................40
What Is Validation? .....................................................................41
Pruning ................................................................................................44
iv
Contents
Machine Learning, Rule Induction, and Statistical Decision
Trees ................................................................................................... 49
Rule Induction ............................................................................ 50
Rule Induction and the Work of Ross Quinlan .......................... 55
The Use of Multiple Trees .......................................................... 57
A Review of the Major Features of Decision Trees .......................... 58
Roots and Trees ......................................................................... 58
Branches..................................................................................... 59
Similarity Measures .................................................................... 59
Recursive Growth....................................................................... 59
Shaping the Decision Tree......................................................... 60
Deploying Decision Trees .......................................................... 60
A Brief Review of the SAS Enterprise Miner ARBORETUM
Procedure................................................................................ 60
Chapter 3 The Mechanics of Decision Tree
Construction ................................................. 63
The Basics of Decision Trees ............................................................ 64
Step 1—Preprocess the Data for the Decision Tree Growing
Engine................................................................................................. 66
Step 2—Set the Input and Target Modeling Characteristics ........... 69
Targets........................................................................................ 69
Inputs .......................................................................................... 71
Step 3—Select the Decision Tree Growth Parameters .................... 72
Step 4—Cluster and Process Each Branch-Forming Input Field .... 74
Clustering Algorithms................................................................. 78
The Kass Merge-and-Split Heuristic ......................................... 86
Dealing with Missing Data and Missing Inputs in Decision
Trees ........................................................................................... 87
Step 5—Select the Candidate Decision Tree Branches................... 90
Step 6—Complete the Form and Content of the Final
Decision Tree..................................................................... 107

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