About The Author
by Kattamuri S. Sarma, PhD
Predictive Modeling with SAS Enterprise Miner, 2nd Edition
- 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
About The Authors
Kattamuri S. Sarma, PhD, is an economist and statistician with 30 years of experience in American business, including stints with IBM and AT&T. He is the founder and president of Ecostat Research Corp., a consulting firm specializing in predictive modeling and forecasting. Over the years, Dr. Sarma has developed predictive models for the banking, insurance, telecommunication, and technology industries. He has been a SAS user since 1992, and he has extensive experience with multivariate statistical methods, econometrics, decision trees, and data mining with neural networks. The author of numerous professional papers and publications, Dr. Sarma is a SAS Certified Professional and a SAS Alliance Partner. He received his bachelor's degree in mathematics and his master's degree in economic statistics from universities in India. Dr. Sarma received his PhD in economics from the University of Pennsylvania, where he worked under the supervision of Nobel Laureate Lawrence R. Klein.
Learn more about this author by visiting his author page at http://support.sas.com/publishing/authors/sarma.html. There you can download free book excerpts, access example code and data, read the latest reviews, get updates, and more.
-
No Comment