Decision Trees for Analytics Using SAS Enterprise Miner

Decision Trees for Analytics Using SAS Enterprise Miner

by Barry de Ville, Padraic Neville
Released July 2013
Publisher(s): SAS Institute
ISBN: 9781629591001

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Book description


Decision Trees for Analytics Using SAS Enterprise Miner is the most comprehensive treatment of decision tree theory, use, and applications available in one easy-to-access place. This book illustrates the application and operation of decision trees in business intelligence, data mining, business analytics, prediction, and knowledge discovery. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements data mining approaches such as regression, as well as other business intelligence applications that incorporate tabular reports, OLAP, or multidimensional cubes.

An expanded and enhanced release of Decision Trees for Business Intelligence and Data Mining Using SAS Enterprise Miner, this book adds up-to-date treatments of boosting and high-performance forest approaches and rule induction. There is a dedicated section on the most recent findings related to bias reduction in variable selection. It provides an exhaustive treatment of the end-to-end process of decision tree construction and the respective considerations and algorithms, and it includes discussions of key issues in decision tree practice.

Analysts who have an introductory understanding of data mining and who are looking for a more advanced, in-depth look at the theory and methods of a decision tree approach to business intelligence and data mining will benefit from this book.

This book is part of the SAS Press program.

Table of contents

  1. Preface
  2. About This Book
  3. About These Authors
  4. Acknowledgments
  5. Chapter 1: Decision Trees—What Are They?
    1. Introduction
    2. Using Decision Trees with Other Modeling Approaches
    3. Why Are Decision Trees So Useful?
    4. Level of Measurement
  6. Chapter 2: Descriptive, Predictive, and Explanatory Analyses
    1. Introduction
    2. The Importance of Showing Context
      1. Antecedents
      2. Intervening Factors
    3. A Classic Study and Illustration of the Need to Understand Context
    4. The Effect of Context
    5. How Do Misleading Results Appear?
      1. Automatic Interaction Detection
    6. The Role of Validation and Statistics in Growing Decision Trees
    7. The Application of Statistical Knowledge to Growing Decision Trees
      1. Significance Tests
    8. Validation to Determine Tree Size and Quality
      1. What Is Validation?
    9. Pruning
    10. Machine Learning, Rule Induction, and Statistical Decision Trees
      1. Rule Induction
      2. Rule Induction and the Work of Ross Quinlan
      3. The Use of Multiple Trees
    11. A Review of the Major Features of Decision Trees
      1. Roots and Trees
      2. Branches
      3. Similarity Measures
      4. Recursive Growth
      5. Shaping the Decision Tree
      6. Deploying Decision Trees
    12. A Brief Review of the SAS Enterprise Miner ARBORETUM Procedure
  7. Chapter 3: The Mechanics of Decision Tree Construction
    1. The Basics of Decision Trees
    2. Step 1—Preprocess the Data for the Decision Tree Growing Engine
    3. Step 2—Set the Input and Target Modeling Characteristics
      1. Targets
      2. Inputs
    4. Step 3—Select the Decision Tree Growth Parameters
    5. Step 4—Cluster and Process Each Branch-Forming Input Field
      1. Clustering Algorithms
      2. The Kass Merge-and-Split Heuristic
      3. Dealing with Missing Data and Missing Inputs in Decision Trees
    6. Step 5—Select the Candidate Decision Tree Branches
    7. Step 6—Complete the Form and Content of the Final Decision Tree
    8. Switching Targets
      1. Example of Multiple Target Selection Using the Home Equity Demonstration Data
      2. Synergy, Functionality, and the Wisdom of the End User
  8. Chapter 4: Business Intelligence and Decision Trees
    1. Introduction
    2. A Decision Tree Approach to Cube Construction
      1. Multidimensional Cubes and Decision Trees Compared: A Small Business Example
      2. Multidimensional Cubes and Decision Trees: A Side-By-Side Comparison
      3. The Main Difference between Decision Trees and Multidimensional Cubes
    3. Regression as a Business Tool
      1. Decision Trees and Regression Compared
    4. Multidimensional Analysis with Trees
      1. An Example with Multiple Targets
  9. Chapter 5: Theoretical Issues in the Decision Tree Growing Process
    1. Introduction
    2. Crafting the Decision Tree Structure for Insight and Exposition
      1. Conceptual Model
      2. Predictive Issues: Accuracy, Reliability, Reproducibility, and Performance
      3. Choosing the Right Number of Branches
    3. Perspectives on Selection Bias
      1. Potential Remedies to Variable Selection Bias
    4. Multiple Decision Trees
      1. Ensembles
  10. Chapter 6: The Integration of Decision Trees with Other Data Mining Approaches
    1. Introduction
    2. Decision Trees in Stratified Regression
      1. Time-Ordered Data
    3. Decision Trees in Forecasting Applications
    4. Decision Trees in Variable Selection
      1. Decision Tree Results
      2. Interactions
      3. Cross-Contributions of Decision Trees and Other Approaches
    5. Decision Trees in Analytical Model Development
    6. The Use of Decision Trees in Rule Induction
      1. Iterative Removal of Observations
    7. Conclusion
      1. Business Intelligence
      2. Data Mining
  11. Glossary
  12. References
  13. Index

Product information

  • Title: Decision Trees for Analytics Using SAS Enterprise Miner
  • Author(s): Barry de Ville, Padraic Neville
  • Release date: July 2013
  • Publisher(s): SAS Institute
  • ISBN: 9781629591001