Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Third Edition

573 Pages·2017·12.45 MB·English

by  Kattamuri S. Sarma

The correct bibliographic citation for this manual is as follows: Sarma, Kattamuri S., Ph.D. 2017. Predictive
Modeling with SAS® Enterprise Miner™: Practical Solutions for Business Applications, Third Edition. Cary, NC:
SAS Institute Inc.

Predictive Modeling with SAS® Enterprise Miner™: Practical Solutions for Business Applications,
Third Edition

Copyright © 2017, SAS Institute Inc., Cary, NC, USA

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Contents

About This Book ..............................................................................................xi

About The Author .......................................................................................... xiii

Chapter 1: Research Strategy .......................................................................... 1

1.1 Introduction .................................................................................................................................... 1
1.2 Types of Inputs ............................................................................................................................... 2
1.2.1 Measurement Scales for Variables ...................................................................................... 2
1.2.2 Predictive Models with Textual Data .................................................................................. 2
1.3 Defining the Target ........................................................................................................................ 2
1.3.1 Predicting Response to Direct Mail .................................................................................... 2
1.3.2 Predicting Risk in the Auto Insurance Industry ................................................................. 4
1.3.3 Predicting Rate Sensitivity of Bank Deposit Products ..................................................... 5
1.3.4 Predicting Customer Attrition ............................................................................................. 7
1.3.5 Predicting a Nominal Categorical (Unordered Polychotomous) Target ......................... 8
1.4 Sources of Modeling Data ........................................................................................................... 10
1.4.1 Comparability between the Sample and Target Universe.............................................. 10
1.4.2 Observation Weights .......................................................................................................... 10
1.5 Pre-Processing the Data ............................................................................................................. 10
1.5.1 Data Cleaning Before Launching SAS Enterprise Miner ................................................ 11
1.5.2 Data Cleaning After Launching SAS Enterprise Miner ................................................... 11
1.6 Alternative Modeling Strategies ................................................................................................. 12
1.6.1 Regression with a Moderate Number of Input Variables ............................................... 12
1.6.2 Regression with a Large Number of Input Variables ...................................................... 13
1.7 Notes ............................................................................................................................................. 13

Chapter 2: Getting Started with Predictive Modeling ..................................... 15

2.1 Introduction ................................................................................................................................... 16
2.2 Opening SAS Enterprise Miner 14.1 ............................................................................................ 16
2.3 Creating a New Project in SAS Enterprise Miner 14.1 .............................................................. 16
2.4 The SAS Enterprise Miner Window ............................................................................................. 17
2.5 Creating a SAS Data Source ........................................................................................................ 18
2.6 Creating a Process Flow Diagram .............................................................................................. 27
2.7 Sample Nodes .............................................................................................................................. 27
2.7.1 Input Data Node ................................................................................................................... 27
2.7.2 Data Partition Node ............................................................................................................. 29
2.7.3 Filter Node ............................................................................................................................ 29
2.7.4 File Import Node .................................................................................................................. 33

Contents iv

2.7.5 Time Series Nodes .............................................................................................................. 36
2.7.6 Merge Node .......................................................................................................................... 50
2.7.7 Append Node ....................................................................................................................... 53
2.8 Tools for Initial Data Exploration ................................................................................................. 56
2.8.1 Stat Explore Node................................................................................................................ 57
2.8.2 MultiPlot Node .................................................................................................................... 64
2.8.3 Graph Explore Node ............................................................................................................ 67
2.8.4 Variable Clustering Node .................................................................................................... 73
2.8.5 Cluster Node ....................................................................................................................... 82
2.8.6 Variable Selection Node..................................................................................................... 85
2.9 Tools for Data Modification ......................................................................................................... 94
2.9.1 Drop Node ............................................................................................................................ 94
2.9.2 Replacement Node .............................................................................................................. 95
2.9.3 Impute Node ......................................................................................................................... 98
2.9.4 Interactive Binning Node .................................................................................................... 99
2.9.5 Principal Components Node ............................................................................................ 106
2.9.6 Transform Variables Node ................................................................................................ 112
2.10 Utility Nodes .............................................................................................................................. 120
2.10.1 SAS Code Node ............................................................................................................... 120
2.11 Appendix to Chapter 2 .............................................................................................................. 126
2.11.1 The Type, the Measurement Scale, and the Number of Levels of a Variable ........... 126
2.11.2 Eigenvalues, Eigenvectors, and Principal Components .............................................. 129
2.11.3 Cramer’s V ........................................................................................................................ 132
2.11.4 Calculation of Chi-Square Statistic and Cramer’s V for a Continuous Input ............ 133
2.12 Exercises .................................................................................................................................... 135
Notes .................................................................................................................................................. 137

Chapter 3: Variable Selection and Transformation of Variables ................... 139

3.1 Introduction ................................................................................................................................. 139
3.2 Variable Selection ....................................................................................................................... 140
3.2.1 Continuous Target with Numeric Interval-scaled Inputs (Case 1) ............................... 140
3.2.2 Continuous Target with Nominal-Categorical Inputs (Case 2) ..................................... 147
3.2.3 Binary Target with Numeric Interval-scaled Inputs (Case 3) ........................................ 153
3.2.4 Binary Target with Nominal-scaled Categorical Inputs (Case 4) ................................. 158
3.3 Variable Selection Using the Variable Clustering Node .......................................................... 162
3.3.1 Selection of the Best Variable from Each Cluster .......................................................... 164
3.3.2 Selecting the Cluster Components .................................................................................. 174
3.4 Variable Selection Using the Decision Tree Node ................................................................... 176
3.5 Transformation of Variables ...................................................................................................... 179
3.5.1 Transform Variables Node ................................................................................................ 179
3.5.2 Transformation before Variable Selection ...................................................................... 181
3.5.3 Transformation after Variable Selection ......................................................................... 183
3.5.4 Passing More Than One Type of Transformation for Each Interval Input to the Next
Node ............................................................................................................................................. 185

v Contents

3.5.5 Saving and Exporting the Code Generated by the Transform Variables Node ........... 189
3.6 Summary ...................................................................................................................................... 190
3.7 Appendix to Chapter 3 ................................................................................................................ 190
3.7.1 Changing the Measurement Scale of a Variable in a Data Source .............................. 190
3.7.2 SAS Code for Comparing Grouped Categorical Variables with the Ungrouped
Variables ...................................................................................................................................... 192
Exercises ............................................................................................................................................ 192
Note .................................................................................................................................................... 193

Chapter 4: Building Decision Tree Models to Predict Response and Risk.... 195

4.1 Introduction ................................................................................................................................. 196
4.2 An Overview of the Tree Methodology in SAS® Enterprise Miner™ ..................................... 196
4.2.1 Decision Trees ................................................................................................................... 196
4.2.2 Decision Tree Models ....................................................................................................... 196
4.2.3 Decision Tree Models vs. Logistic Regression Models ................................................ 198
4.2.4 Applying the Decision Tree Model to Prospect Data .................................................... 198
4.2.5 Calculation of the Worth of a Tree ................................................................................... 199
4.2.6 Roles of the Training and Validation Data in the Development of a Decision Tree .... 201
4.2.7 Regression Tree ................................................................................................................ 202
4.3 Development of the Tree in SAS Enterprise Miner .................................................................. 202
4.3.1 Growing an Initial Tree ...................................................................................................... 202
4.3.2 P-value Adjustment Options ............................................................................................ 209
4.3.3 Controlling Tree Growth: Stopping Rules ....................................................................... 211
4.3.3.1 Controlling Tree Growth through the Split Size Property .......................................... 211
4.3.4 Pruning: Selecting the Right-Sized Tree Using Validation Data ................................... 211
4.3.5 Step-by-Step Illustration of Growing and Pruning a Tree ............................................. 213
4.3.6 Average Profit vs. Total Profit for Comparing Trees of Different Sizes ....................... 218
4.3.7 Accuracy /Misclassification Criterion in Selecting the Right-sized Tree (Classification
of Records and Nodes by Maximizing Accuracy) ................................................................... 218
4.3.8 Assessment of a Tree or Sub-tree Using Average Square Error ................................. 220
4.3.9 Selection of the Right-sized Tree .................................................................................... 220
4.4 Decision Tree Model to Predict Response to Direct Marketing ............................................ 221
4.4.1 Testing Model Performance with a Test Data Set ......................................................... 230
4.4.2 Applying the Decision Tree Model to Score a Data Set ................................................ 231
4.5 Developing a Regression Tree Model to Predict Risk ............................................................ 236
4.5.1 Summary of the Regression Tree Model to Predict Risk .............................................. 243
4.6 Developing Decision Trees Interactively .................................................................................. 244
4.6.1 Interactively Modifying an Existing Decision Tree ......................................................... 244
4.6.3 Developing the Maximal Tree in Interactive Mode ........................................................ 266
4.7 Summary ...................................................................................................................................... 269
4.8 Appendix to Chapter 4 ................................................................................................................ 270
4.8.1 Pearson’s Chi-Square Test ............................................................................................... 270
4.8.2 Calculation of Impurity Reduction using Gini Index ...................................................... 271
4.8.3 Calculation of Impurity Reduction/Information Gain using Entropy ............................ 272
4.8.4 Adjusting the Predicted Probabilities for Over-sampling ............................................. 274

Contents vi

4.8.5 Expected Profits Using Unadjusted Probabilities .......................................................... 275
4.8.6 Expected Profits Using Adjusted Probabilities .............................................................. 275
4.9 Exercises ...................................................................................................................................... 275
Notes .................................................................................................................................................. 277

Chapter 5: Neural Network Models to Predict Response and Risk ............... 279

5.1 Introduction ................................................................................................................................. 280
5.1.1 Target Variables for the Models ....................................................................................... 280
5.1.2 Neural Network Node Details .......................................................................................... 281
5.2 General Example of a Neural Network Model .......................................................................... 281
5.2.1 Input Layer ......................................................................................................................... 282
5.2.2 Hidden Layers .................................................................................................................... 283
5.2.3 Output Layer or Target Layer ........................................................................................... 288
5.2.4 Activation Function of the Output Layer ........................................................................ 289
5.3 Estimation of Weights in a Neural Network Model .................................................................. 290
5.4 Neural Network Model to Predict Response ........................................................................... 291
5.4.1 Setting the Neural Network Node Properties ................................................................. 293
5.4.2 Assessing the Predictive Performance of the Estimated Model ................................. 297
5.4.3 Receiver Operating Characteristic (ROC) Charts ......................................................... 300
5.4.4 How Did the Neural Network Node Pick the Optimum Weights for This Model? ....... 303
5.4.5 Scoring a Data Set Using the Neural Network Model ................................................... 305
5.4.6 Score Code ....................................................................................................................... 308
5.5 Neural Network Model to Predict Loss Frequency in Auto Insurance .................................. 308
5.5.1 Loss Frequency as an Ordinal Target ............................................................................ 309
5.5.1.1 Target Layer Combination and Activation Functions ................................................ 311
5.5.3 Classification of Risks for Rate Setting in Auto Insurance with Predicted
Probabilities ................................................................................................................................ 321
5.6 Alternative Specifications of the Neural Networks ................................................................. 322
5.6.1 A Multilayer Perceptron (MLP) Neural Network ............................................................. 322
5.6.2 Radial Basis Function (RBF) Neural Network ................................................................. 324
5.7 Comparison of Alternative Built-in Architectures of the Neural Network Node .................. 330
5.7.1 Multilayer Perceptron (MLP) Network ............................................................................. 332
5.7.2 Ordinary Radial Basis Function with Equal Heights and Widths (ORBFEQ) ............... 333
5.7.3 Ordinary Radial Basis Function with Equal Heights and Unequal Widths (ORBFUN) 335
5.7.4 Normalized Radial Basis Function with Equal Widths and Heights (NRBFEQ) .......... 338
5.7.5 Normalized Radial Basis Function with Equal Heights and Unequal Widths
(NRBFEH) ..................................................................................................................................... 340
5.7.6 Normalized Radial Basis Function with Equal Widths and Unequal Heights
(NRBFEW) .................................................................................................................................... 343
5.7.7 Normalized Radial Basis Function with Equal Volumes (NRBFEV) ............................. 346
5.7.8 Normalized Radial Basis Function with Unequal Widths and Heights (NRBFUN) ..... 348
5.7.9 User-Specified Architectures ........................................................................................... 351
5.8 AutoNeural Node ......................................................................................................................... 354
5.9 DMNeural Node ........................................................................................................................... 356
5.10 Dmine Regression Node .......................................................................................................... 358

vii Contents

5.11 Comparing the Models Generated by DMNeural, AutoNeural, and Dmine Regression
Nodes ................................................................................................................................................. 360
5.12 Summary .................................................................................................................................... 362
5.13 Appendix to Chapter 5 .............................................................................................................. 363
5.14 Exercises .................................................................................................................................... 365
Notes .................................................................................................................................................. 367

Chapter 6: Regression Models ..................................................................... 369

6.1 Introduction ................................................................................................................................. 369
6.2 What Types of Models Can Be Developed Using the Regression Node? ............................. 369
6.2.1 Models with a Binary Target ............................................................................................ 369
6.2.2 Models with an Ordinal Target ......................................................................................... 373
6.2.3 Models with a Nominal (Unordered) Target .................................................................... 379
6.2.4 Models with Continuous Targets ..................................................................................... 383
6.3 An Overview of Some Properties of the Regression Node ..................................................... 383
6.3.1 Regression Type Property ................................................................................................ 384
6.3.2 Link Function Property ...................................................................................................... 384
6.3.3 Selection Model Property ................................................................................................. 386
6.3.4 Selection Criterion Property5 ........................................................................................... 403
6.4 Business Applications ................................................................................................................ 415
6.4.1 Logistic Regression for Predicting Response to a Mail Campaign ............................. 417
6.4.2 Regression for a Continuous Target ............................................................................... 431
6.5 Summary ...................................................................................................................................... 442
6.6 Appendix to Chapter 6 ................................................................................................................ 443
6.6.1 SAS Code ........................................................................................................................... 443
6.6.2 Examples of the selection criteria when the Model Selection property set to
Forward. ...................................................................................................................................... 447
6.7 Exercises ...................................................................................................................................... 451
Notes .................................................................................................................................................. 452

Chapter 7: Comparison and Combination of Different Models ..................... 453

7.1 Introduction ................................................................................................................................ 453
7.2 Models for Binary Targets: An Example of Predicting Attrition ............................................ 454
7.2.1 Logistic Regression for Predicting Attrition ................................................................... 456
7.2.2 Decision Tree Model for Predicting Attrition ................................................................. 458
7.2.3 A Neural Network Model for Predicting Attrition .......................................................... 460
7.3 Models for Ordinal Targets: An Example of Predicting the Risk of Accident Risk .............. 464
7.3.1 Lift Charts and Capture Rates for Models with Ordinal Targets ................................. 465
7.3.2 Logistic Regression with Proportional Odds for Predicting Risk in Auto Insurance . 466
7.3.3 Decision Tree Model for Predicting Risk in Auto Insurance ........................................ 469
7.3.4 Neural Network Model for Predicting Risk in Auto Insurance ...................................... 473
7.4 Comparison of All Three Accident Risk Models ...................................................................... 476
7.5 Boosting and Combining Predictive Models ............................................................................ 476
7.5.1 Gradient Boosting ............................................................................................................. 477
7.5.2 Stochastic Gradient Boosting .......................................................................................... 479
7.5.3 An Illustration of Boosting Using the Gradient Boosting Node .................................... 479

Contents viii

7.5.4 The Ensemble Node .......................................................................................................... 482
7.5.5 Comparing the Gradient Boosting and Ensemble Methods of Combining Models ... 485
7.6 Appendix to Chapter 7 ................................................................................................................ 486
7.6.1 Least Squares Loss ........................................................................................................... 486
7.6.2 Least Absolute Deviation Loss......................................................................................... 486
7.6.3 Huber-M Loss .................................................................................................................... 487
7.6.4 Logit Loss ........................................................................................................................... 487
7.7 Exercises ...................................................................................................................................... 488
Note .................................................................................................................................................... 488

Chapter 8: Customer Profitability ................................................................ 489

8.1 Introduction ................................................................................................................................. 489
8.2 Acquisition Cost .......................................................................................................................... 491
8.3 Cost of Default ............................................................................................................................ 492
8.5 Profit ............................................................................................................................................. 493
8.6 The Optimum Cutoff Point ......................................................................................................... 495
8.7 Alternative Scenarios of Response and Risk ........................................................................... 496
8.8 Customer Lifetime Value ............................................................................................................ 496
8.9 Suggestions for Extending Results ........................................................................................... 497
Note .................................................................................................................................................... 497

Chapter 9: Introduction to Predictive Modeling with Textual Data .............. 499

9.1 Introduction ................................................................................................................................. 499
9.1.1 Quantifying Textual Data: A Simplified Example ............................................................ 500
9.1.2 Dimension Reduction and Latent Semantic Indexing ................................................... 503
9.1.3 Summary of the Steps in Quantifying Textual Information ........................................... 506
9.2 Retrieving Documents from the World Wide Web ................................................................... 507
9.2.1 The %TMFILTER Macro .................................................................................................... 507
9.3 Creating a SAS Data Set from Text Files .................................................................................. 509
9.4 The Text Import Node ................................................................................................................. 512
9.5 Creating a Data Source for Text Mining ................................................................................... 514
9.6 Text Parsing Node....................................................................................................................... 516
9.7 Text Filter Node ........................................................................................................................... 521
9.7.1 Frequency Weighting ........................................................................................................ 521
9.7.2 Term Weighting ................................................................................................................. 521
9.7.3 Adjusted Frequencies ....................................................................................................... 521
9.7.4 Frequency Weighting Methods ........................................................................................ 521
9.7.5 Term Weighting Methods ................................................................................................. 523
9.8 Text Topic Node .......................................................................................................................... 528
9.8.1 Developing a Predictive Equation Using the Output Data Set Created by the Text
Topic Node .................................................................................................................................. 533
9.9 Text Cluster Node ....................................................................................................................... 534
9.9.1 Hierarchical Clustering ..................................................................................................... 535
9.9.2 Expectation-Maximization (EM) Clustering .................................................................... 536
9.9.3 Using the Text Cluster Node ............................................................................................ 542

ix Contents

9.10 Exercises .................................................................................................................................... 546
Notes .................................................................................................................................................. 546

Index ........................................................................................................... 547

Contents x

About This Book

What Does This Book Cover?

The book shows how to rapidly develop and test predictive models using SAS® Enterprise Miner™. Topics
include Logistic Regression, Regression, Decision Trees, Neural Networks, Variable Clustering,
Observaton-Clustering, Data Imputation, Binning, Data Exploration, Variable Selection, Variable
Transformation, Modeling Binary and continuous targets, Analysis of textual data, Eigenvalues,
Eigenvectors and principal components, Gradient Boosting, Ensemble, Time Series Data Preparation, Time
Series Dimension Reduction, Time Series Similarity and importing external data into SAS Enterprise
Miner. The book demonstrates various methods using simple examples and shows how to apply them to
real-world business data using SAS Enterprise Miner. It integrates theoretical explanations with the
computations done by various SAS nodes. The examples include manual computations with simple
examples as well computations done using SAS code with real data sets from different businesses.

Support Vector Machines and Association rules are not covered in this book.

Is This Book for You?

If you are a business analyst, a student trying to learn predictive modeling using SAS Enterprise Miner, a
data scientist who wants process data efficiently and build predictive models, this book is for you. If you
want to learn how to select key variables, test a variety of models quickly and develop robust predictive
models in a short period of time using SAS Enterprise Miner, this book gives you step-by-step guidance
with simple explanation of the procedures and the underlying theory.

What Are the Prerequisites for This Book?

●
Elementary algebra and basic training (equivalent to one to two semesters of course work) in
statistics covering inference, hypothesis testing, probability and regression

●
Experience with Base SAS® software and some understanding of simple SAS macros and macro
variables.

What’s New in This Edition?

The book is updated to the latest version of SAS Enterprise Miner. The time series section is enhanced.
Time Series Exponential Smoothing, Time Series Correlation, Time Series Dimension Reduction and Time
Series Similarity nodes are added. Examples of calculating the information gain of node splits using Gini
index and Entropy measures are included. More examples are added to describe the process of model
selection in the regression node.

What Should You Know about the Examples?

Realistic business examples are used. You need SAS Enterprise Miner so that you can read the book and
try the examples simultaneously.

Software Used to Develop the Book's Content
SAS Enterprise Miner

xii

Example Code and Data
You can access the example code and data for this book by linking to its author page at
https://support.sas.com/authors.

Output and Graphics
Almost all the graphics are generated by SAS Enterprise Miner. A few graphs are generated by
SAS/GRAPH® Software.

Where Are the Exercise Solutions?

Exercise solutions are posted on the author page at https://support.sas.com/authors.

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