Book description
Learn the theory behind and methods for predictive modeling using SAS Enterprise Miner.
Learn how to produce predictive models and prepare presentation-quality graphics in record time with Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition.
If you are a graduate student, researcher, or statistician interested in predictive modeling; a data mining expert who wants to learn SAS Enterprise Miner; or a business analyst looking for an introduction to predictive modeling using SAS Enterprise Miner, you'll be able to develop predictive models quickly and effectively using the theory and examples presented in this book.
Author Kattamuri Sarma offers the theory behind, programming steps for, and examples of predictive modeling with SAS Enterprise Miner, along with exercises at the end of each chapter. You'll gain a comprehensive awareness of how to find solutions for your business needs. This second edition features expanded coverage of the SAS Enterprise Miner nodes, now including File Import, Time Series, Variable Clustering, Cluster, Interactive Binning, Principal Components, AutoNeural, DMNeural, Dmine Regression, Gradient Boosting, Ensemble, and Text Mining.
Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis. Do it all with Predictive Modeling with SAS Enterprise Miner.
This book is part of the SAS Press program.
Table of contents
- Preface
- About This Book
- About The Author
- Acknowledgments
- Chapter 1: Research Strategy
-
Chapter 2: Getting Started with Predictive Modeling
- 2.1 Introduction
- 2.2 Opening SAS Enterprise Miner 12.1
- 2.3 Creating a New Project in SAS Enterprise Miner 12.1
- 2.4 The SAS Enterprise Miner Window
- 2.5 Creating a SAS Data Source
- 2.6 Creating a Process Flow Diagram
- 2.7 Sample Nodes
- 2.8 Tools for Initial Data Exploration
- 2.9 Tools for Data Modification
- 2.10 Utility Nodes
- 2.11 Appendix to Chapter 2
- 2.12 Exercises
- Chapter 3: Variable Selection and Transformation of Variables
-
Chapter 4: Building Decision Tree Models to Predict Response and Risk
- 4.1 Introduction
-
4.2 An Overview of the Tree Methodology in SAS Enterprise Miner
- 4.2.1 Decision Trees
- 4.2.2 Decision Tree Models
- 4.2.3 Decision Tree Models vs. Logistic Regression Models
- 4.2.4 Applying the Decision Tree Model to Prospect Data
- 4.2.5 Calculation of the Worth of a Tree
- 4.2.6 Roles of the Training and Validation Data in the Development of a Decision Tree
- 4.2.7 Regression Tree
-
4.3 Development of the Tree in SAS Enterprise Miner
- 4.3.1 Growing an Initial Tree
- 4.3.2 P-value Adjustment Options
- 4.3.3 Controlling Tree Growth: Stopping Rules
- 4.3.4 Pruning: Selecting the Right-Sized Tree Using Validation Data
- 4.3.5 Step-by-Step Illustration of Growing and Pruning a Tree
- 4.3.6 Average Profit vs. Total Profit for Comparing Trees of Different Sizes
- 4.3.7 Accuracy /Misclassification Criterion in Selecting the Right-sized Tree (Classification of Records and Nodes by Maximizing Accuracy)
- 4.3.8 Assessment of a Tree or Sub-tree Using Average Square Error
- 4.3.9 Selection of the Right-sized Tree
- 4.4 A Decision Tree Model to Predict Response to Direct Marketing
- 4.5 Developing a Regression Tree Model to Predict Risk
- 4.6 Developing Decision Trees Interactively
- 4.7 Summary
- 4.8 Appendix to Chapter 4
- 4.9 Exercises
-
Chapter 5: Neural Network Models to Predict Response and Risk
- 5.1 Introduction
- 5.2 A General Example of a Neural Network Model
- 5.3 Estimation of Weights in a Neural Network Model
-
5.4 A Neural Network Model to Predict Response
- 5.4.1 Setting the Neural Network Node Properties
- 5.4.2 Assessing the Predictive Performance of the Estimated Model
- 5.4.3 Receiver Operating Characteristic (ROC) Charts
- 5.4.4 How Did the Neural Network Node Pick the Optimum Weights for This Model?
- 5.4.5 Scoring a Data Set Using the Neural Network Model
- 5.4.6 Score Code
- 5.5 A Neural Network Model to Predict Loss Frequency in Auto Insurance
- 5.6 Alternative Specifications of the Neural Networks
-
5.7 Comparison of Alternative Built-in Architectures of the Neural Network Node
- 5.7.1 Multilayer Perceptron (MLP) Network
- 5.7.2 Ordinary Radial Basis Function with Equal Heights and Widths (ORBFEQ)
- 5.7.3 Ordinary Radial Basis Function with Equal Heights and Unequal Widths (ORBFUN)
- 5.7.4 Normalized Radial Basis Function with Equal Widths and Heights (NRBFEQ)
- 5.7.5 Normalized Radial Basis Function with Equal Heights and Unequal Widths (NRBFEH)
- 5.7.6 Normalized Radial Basis Function with Equal Widths and Unequal Heights (NRBFEW)
- 5.7.7 Normalized Radial Basis Function with Equal Volumes (NRBFEV)
- 5.7.8 Normalized Radial Basis Function with Unequal Widths and Heights (NRBFUN)
- 5.7.9 User-Specified Architectures
- 5.8 AutoNeural Node
- 5.9 DMNeural Node
- 5.10 Dmine Regression Node
- 5.11 Comparing the Models Generated by DMNeural, AutoNeural, and Dmine Regression Nodes
- 5.12 Summary
- 5.13 Appendix to Chapter 5
- 5.14 Exercises
- Chapter 6: Regression Models
- Chapter 7: Comparison and Combination of Different Models
- Chapter 8: Customer Profitability
- Chapter 9: Introduction to Predictive Modeling with Textual Data
- Index
Product information
- Title: Predictive Modeling with SAS Enterprise Miner, 2nd Edition
- Author(s):
- Release date: December 2013
- Publisher(s): SAS Institute
- ISBN: 9781607648185
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