Book description
A step-by-step guide to predictive modeling! Kattamuri Sarma's Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Third Edition, will show you how to develop and test predictive models quickly using SAS Enterprise Miner. Using realistic data, the book explains complex methods in a simple and practical way to readers from different backgrounds and industries. Incorporating the latest version of Enterprise Miner, this third edition also expands the section on time series. Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. Topics covered include logistic regression, regression, decision trees, neural networks, variable clustering, observation clustering, data imputation, binning, data exploration, variable selection, variable transformation, and much more, including analysis of textual data. 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!Table of contents
- About This Book
- About The Author
- Chapter 1: Research Strategy
- 1.1 Introduction
- 1.2 Types of Inputs
- 1.3 Defining the Target
- 1.4 Sources of Modeling Data
- 1.5 Pre-Processing the Data
- 1.6 Alternative Modeling Strategies
- 1.7 Notes
- Chapter 2: Getting Started with Predictive Modeling
- 2.1 Introduction
- 2.2 Opening SAS Enterprise Miner 14.1
- 2.3 Creating a New Project in SAS Enterprise Miner 14.1
- 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
- Notes
- Chapter 3: Variable Selection and Transformation of Variables
- 3.1 Introduction
- 3.2 Variable Selection
- 3.3 Variable Selection Using the Variable Clustering Node
- 3.4 Variable Selection Using the Decision Tree Node
- 3.5 Transformation of Variables
- 3.6 Summary
- 3.7 Appendix to Chapter 3
- Exercises
- Note
- 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
- 4.3.3.1 Controlling Tree Growth through the Split Size Property
- 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 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
- 4.8.1 Pearson’s Chi-Square Test
- 4.8.2 Calculation of Impurity Reduction using Gini Index
- 4.8.3 Calculation of Impurity Reduction/Information Gain using Entropy
- 4.8.4 Adjusting the Predicted Probabilities for Over-sampling
- 4.8.5 Expected Profits Using Unadjusted Probabilities
- 4.8.6 Expected Profits Using Adjusted Probabilities
- 4.9 Exercises
- Notes
- Chapter 5: Neural Network Models to Predict Response and Risk
- 5.1 Introduction
- 5.2 General Example of a Neural Network Model
- 5.3 Estimation of Weights in a Neural Network Model
-
5.4 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 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 Node
- 5.12 Summary
- 5.13 Appendix to Chapter 5
- 5.14 Exercises
- Notes
- Chapter 6: Regression Models
- 6.1 Introduction
- 6.2 What Types of Models Can Be Developed Using the Regression Node?
- 6.3 An Overview of Some Properties of the Regression Node
- 6.4 Business Applications
- 6.5 Summary
- 6.6 Appendix to Chapter 6
- 6.7 Exercises
- Notes
- Chapter 7: Comparison and Combination of Different Models
- 7.1 Introduction
- 7.2 Models for Binary Targets: An Example of Predicting Attrition
- 7.3 Models for Ordinal Targets: An Example of Predicting the Risk of Accident Risk
- 7.4 Comparison of All Three Accident Risk Models
- 7.5 Boosting and Combining Predictive Models
- 7.6 Appendix to Chapter 7
- 7.7 Exercises
- Note
- Chapter 8: Customer Profitability
- 8.1 Introduction
- 8.2 Acquisition Cost
- 8.3 Cost of Default
- 8.5 Profit
- 8.6 The Optimum Cutoff Point
- 8.7 Alternative Scenarios of Response and Risk
- 8.8 Customer Lifetime Value
- 8.9 Suggestions for Extending Results
- Note
- Chapter 9: Introduction to Predictive Modeling with Textual Data
- 9.1 Introduction
- 9.2 Retrieving Documents from the World Wide Web
- 9.3 Creating a SAS Data Set from Text Files
- 9.4 The Text Import Node
- 9.5 Creating a Data Source for Text Mining
- 9.6 Text Parsing Node
- 9.7 Text Filter Node
- 9.8 Text Topic Node
- 9.9 Text Cluster Node
- 9.10 Exercises
- Notes
- Index
Product information
- Title: Predictive Modeling with SAS Enterprise Miner, 3rd Edition
- Author(s):
- Release date: July 2017
- Publisher(s): SAS Institute
- ISBN: 9781635260380
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