Predictive Modeling with SAS Enterprise Miner by Jungpin Wu

Praise from the Experts "The author is to be congratulated and appreciated for writing such a practical and useful book for both entry-level and seasoned practitioners. It provides very clear step-by-step instructions using various case examples. In addition, it also provides the explanation of essential theoretical background in each modeling technique." Carl Lee, Ph.D. Professor of Statistics Data Mining Program Coordinator Central Michigan University

"Kattamuri Sarma has written a practical guide that will be o f use to modeling professionals of all backgrounds and levels of experience. In it he shows us the power o f SAS Enterprise Miner to enhance the modeling process in a way that is straightforward, easy to follow, and thorough. From data scrubbing to model fitting, each step in the model building process is described in a clear manner. The book draws on the author's extensive real-world experience to illustrate the ways in which SAS Enterprise Miner can help to facilitate the modeling process." Lee Medoff SAS User 'The content is wonderful, clear, and thorough.' Andrea Wainwright-Zimmerman Senior Statistical Analysis Manager

" I think this book will be very helpful to new users o f SAS Enterprise Miner. It presents many examples in a way that puts the material across clearly. I especially liked the chapters on decision trees and neural networks for their detailed exposition. I also was pleased to see many cases where the author shows the SAS code behind the scenes. I think Dr. Sarma did a fine job." Rich Perline, Ph.D. Senior Scientist NuTech Solutions, Inc.

Predictive Modeling with SAS* Enterprise Minerâ&#x201E;˘ Practical Solutions for Business Applications

Kattamuri S. Sarma, Ph.D.

The correct bibliographic citation for this manual is as follows: Sarma, Kattamuri S. 2007. Predictive Modeling with SAS* Enterprise Miner": Practical Solutions for Business Applications. Cary, NC: SAS Institute Inc.

Predictive Modeling with SAS* Enterprise Miner ": Practical Solutions for Business Applications Copyright ÂŠ 2007, SAS InstituteInc., Cary, NC, USA ISBN 978-1-59047-703-8 All rights reserved. Produced in the United States of America. For a hard-copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. For a Web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227-19, Commercial Computer Software-Restricted Rights (June 1987). SAS Institute Inc., SAS Campus Drive, Cary, North Carolina 27513. 1st printing, September 2007 2nd printing, May 2009 3rd printing, December 2009 SAS* Publishing provides a complete selection of books and electronic products to help customers use SAS software to its fullest potential. For more information about our e-books, e-learning products, CDs, and hard-copy books, visit the SAS Publishing Web site at support.sas.com/publishing or call 1-800-727-3228. SAS* and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ÂŽ indicatesUSA registration. Other brand and product names are registered trademarks or trademarks of their respective companies.

Contents Preface Acknowledgments

Chapter 1 Research Strategy 1.1 1.2 1.3 1.4 1.5 1.6

Introduction Measurement Scales for Variables Defining the Target Sources of Modeling Data Pre-Processing the Data Alternative Modeling Strategies

Chapter 2 Getting Started w i t h Predictive Modeling 2.1 Introduction 2.2 Opening SAS Enterprise Miner 5.2 2.3 Creating a New Project in SAS Enterprise Miner 5.2 2.4 The SAS Enterprise Miner Window 2.5 Creating a SAS Data Source 2.6 Creating a Process Flow Diagram 2.7 Summary 2.8 Appendix to Chapter 2

vii xi

1 1 2 2 11 12 14

17 17 18 20 21 22 35 72 72

Chapter 3 Variable Selection and Transformation off Variables 77 3.1 3.2 3.3 3.4 3.5

Introduction Variable Selection Transformation of Variables Summary Appendix to Chapter 3

Chapter 4 Building Decision Tree Models t o Predict Response and Risk

77 78 99 109 110

113

4.1 Introduction 113 4.2 An Overview of the Tree Methodology in SAS Enterprise Miner 114 4.3 Development of the Tree in SAS Enterprise Miner... 121

iv Contents

4.4 A Decision Tree Model to Predict Response to Direct Marketing 4.5 Developing a Regression Tree Model to Predict Risk 4.6 Summary 4.7 Appendix to Chapter 4

Chapter 5

Neural Network Models t o Predict Response and Risk 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9

143 159 165 166

169

Introduction A General Example of a Neural Network Model Estimation of Weights in a Neural Network Model... A Neural Network Model to Predict Response A Neural Network Model to Predict Loss Frequency in Auto Insurance An Introduction to Radial Basis Functions Alternative Specifications of the Neural Network Architecture Summary Appendix to Chapter 5

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 Appendix to Chapter 6

Chapter 7 Comparison 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 Accident Risk 7.4 Comparison of All Three Accident Risk Models

169 172 180 181 204 216 219 231 232

235 235 236 249 273 301

305 305 305 316 327

Contents v

Chapter 8 Customer Profitability 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9

Glossary..... References Index

329

Introduction Acquisition Cost Cost of Default Revenue Profit The Optimum Cut-off Point Alternative Scenarios of Response and Risk Customer Lifetime Value Suggestions for Extending Results

329 331 333 334 334 336 337 338 338

339 347 349

Preface In order to make effective use of the tools provided in SAS Enterprise Miner, you need to be able to do more than just set the software in motion. While it is essential to know the mechanics o f how to use each tool (or node, as Enterprise Miner tools are called), you should also understand the methodology behind each tool and be able to interpret the output produced by each tool and the multitude of options available with each one. Although this book will appeal to beginners because of its step-by-step, screen-by-screen introduction to the basic tasks that can be accomplished with Enterprise Miner, it also provides the depth and background needed to master many of the more complex but rewarding concepts contained within this versatile product. The book begins by introducing the basics of creating a project, manipulating data sources, choosing the right property values for each node, and navigating through different results windows. It then demonstrates various pre-processing tools required for building predictive models before beginning its treatment of the three main predictive modeling toolsâ&#x20AC;&#x201D;Decision Tree, Neural Network, and Regression. These are addressed in considerable detail, with numerous examples o f practical business applications that are illustrated with tables, charts, displays, equations, and even manual calculations that let you see the essence of what Enterprise Miner is doing as it estimates or optimizes a given model. By the time you finish with this book, Enterprise Miner will no longer be a "black box": you will have an in-depth understanding of the product's inner workings. Throughout the book, the link between the output generated by Enterprise Miner and the statistical theory behind the business analysis for which Enterprise Miner is being used is explicitly shown. Even the SAS code generated by each node is examined line by line to show the correspondence between the theory and the results produced by Enterprise Miner. In many places, however, intuitive explanations are used to give you an appreciation of the way that various nodes such as Decision Tree, Neural Network, Regression, and Variable Selection operate and how different options such as Model Selection Criteria and Model Assessment are implemented. These explanations are intended not to replicate the exact steps that SAS uses internally to make these computations, but to give a good practical sense o f how these tools work in general. Overall, I believe this approach will help you use the tools in Enterprise Miner with greater comprehension and confidence. Several examples o f business questions drawn from the insurance and banking industries and based on simulated, but realistic, data are used to illustrate the Enterprise Miner tools. However, the procedures discussed are relevant for any industry. Tables and graphs from the output data sets created by various nodes are also included to give you an idea o f how to make custom tables from the results produced by Enterprise Miner. In the end you should have gained enough understanding o f Enterprise Miner to become comfortable and innovative in adapting the applications discussed here to solve your own business problems.

Chapter Details Chapter 1 discusses research strategy. I include general issues such as defining the target population, defining the target (or dependent) variable, collecting data, cleaning the data, and selecting an appropriate model.

Preface

Chapter 2 shows how to open Enterprise Miner, start a new project, and create data sources. It shows various components of the Enterprise Miner window and shows how to create a process flow diagram. In this chapter, I use example data sets to demonstrate in detail how to use the Input Data, StatExplore, MultiPIot, Impute, Data Partition, Filter, Variable Selection, Transform Variables, SAS Code, and Drop nodes. I also discuss the output and SAS code generated by some of these nodes. I manually compute certain statistics such as Cramer's V and compare the results with those produced by StatExplore. Chapter 3 covers the Variable Selection and Transform Variables nodes in detail. In using the Variable Selection node you have a choice of many options, depending on the type of target and the measurement scale of the inputs. To help make things clear, I illustrate each situation with a separate data set. Chapter 4 discusses decision trees and regression trees. First I present the general tree methodology, and, using a simple example, I manually work through the sequence of s t e p s — growing the tree, classifying the nodes, and pruning—whichis performed by the Decision Tree node. I then show how decision tree models are built for predicting response and risk by presenting two examples based on a hypothetical auto insurance company. The first model predicts the probability of response to a mail order campaign. The second model predicts risk as measured by claim frequency, and since claim frequency is measured as a continuous variable, the model built in this case is a regression tree. A detailed discussion of the SAS code generated by the Decision Tree node is included at the end of the chapter. Chapter 5 provides an introduction to neural networks. Here I try to demystify the neural networks methodology by giving an intuitive explanation using simple algebra. I show how to configure neural networks to be consistent with economic and statistical theory and how to interpret the results correctly. The neural network architecture—input layer, hidden layers, and output layer—is illustrated algebraicallywith numerical examples. Although the formulas presented here may look complex, they do not require a high-level knowledge of mathematics, and patience in working through them will be rewarded with a thorough understanding o f neural networks and their applications. In this chapter, the iterative processes of estimation of the model using the training data set as well as the selection of the optimal weights for the model using the validation data set are first discussed intuitively. Next, explicit numerical examples are given to clarify each step. As in Chapter 4, two models are developed using the hypothetical insurance data—aresponse model with a binary target, and a risk model with (in this case) an ordinal target representing accident frequency. I examine line by line the SAS code generated by the Neural Network node and show the correspondence between the theory and the results produced by Enterprise Miner. Chapter 6 demonstrates how to develop logistic regression models for targets with different measurement scales: binary, categorical with more than two categories, ordinal, and continuous (interval-scaled). Using an example data set with a binary target, I demonstrate various model selection criteria and model selection methods. I also present business applications from the banking industry involving two predictive models, one with a binary target and one with a continuous target. The model with a binary target predicts the probability of response to a mail campaign while the model with a continuous target predicts the increase in deposits that is due to an interest rate increase. This chapter also shows how to calculate the lift and capture rates of the models when the target is continuous.

Preface ix

In Chapter 7,1 compare the results of three modeling tools—DecisionTree, Neural Network, and Regression—that were presented in earlier chapters. For this purpose I develop two predictive models and then take turns applying the three modeling tools to each model. The first model has a binary target and predicts the probability of customer attrition for a fictitious bank. The second model has an ordinal target, which is a discrete version of a continuous variable, and predicts risk (as measured by loss frequency) for a fictitious auto insurance company. This chapter also provides a method of computing the lift and capture rates of these models using the expected value of the target variable. Chapter 8 shows how to calculate profitability for each of the ten deciles created when a data set of prospective customers is scored using the output of the modeling process. It then shows how to use these profitability estimates to address questions such as how to choose an optimum cut-off point for a mailing campaign. Here my objective is to introduce the notion of the marginal cost and marginal revenue associated with risk and response and to show how they can be used to make rational quantitative decisions in the marketing sphere.

How t o Use t h e Book •

To get the most out of this book, open Enterprise Miner and follow the sequence of tasks performed in each chapter, using either the data sets provided op this book's companion Web site or, even better, your own data sets. See support.sas.com/companionsites.

•

Work through the manual calculations as well as the mathematical derivations presented in the book to get an in-depth understanding of the logic behind different models.

•

To learn predictive modeling, read the general explanation and the theory, and then follow the steps given in the book to develop models using either the data sets provided on this book's companion Web site or your own data sets. Try variations of what is done in the book to strengthen your understanding of the topics covered.

•

I f you already know Enterprise Miner and want to get a good understanding of decision trees and neural networks, focus on the examples and detailed derivations given in Chapters 4, 5, and 6. These derivations are not as complex as they appear to be.

Prerequisites •

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

•

Familiarity with measurement scales of variables—continuous, categorical,ordinal, etc.

•

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

Acknowledgments I would like to thank a number o f people who helped me at various stages during the writing of this book. At the beginning of this project, I had many discussions about practical aspects of modeling techniques, especially neural networks and decision trees, with Ravindra Sarma, who was at that time a graduate student at MIT working on a neural networks project. In addition, he read the manuscript at several stages and contributed editorial assistance, as well as many in-depth questions and insightful comments that greatly improved the text. While I was working on SAS Enterprise Miner projects, I turned several times to SAS Technical Support. In addition to always answering my questions about how to run the software, they often provided informative explanations of the methodology behind it. I learned a great deal by sending questions to them and getting solid answers. I do not have a list of all the people I talked with in SAS Technical Support, so I would like to recognize them as a group. I would like to thank Randal Blank, a friend and a senior associate at KPMG LLP, for his constant encouragement and editorial help. Chris Monroe, a colleague from my days at A T & T , provided valuable editorial assistance on several sections. In addition, I would like to thank seven reviewers from SASâ&#x20AC;&#x201D;Fang Chen, Brent Cohen,David Duling, Leonardo Auslender, Mary Grace Crissey, R. Wayne Thompson, and Rob Agnelliâ&#x20AC;&#x201D;and two outside reviewers, Goutam Chakraborty and Michael Szenberg. I could not have written this book without the encouragement and support of Julie Piatt and Patsy Poole at SAS Publishing. Julie was the supervisor of the project, and Patsy worked closely with me and was very helpful with organization and editing. Other members of the SAS Publishing team who made indispensable contributions were Caroline Brickley, who edited the book, Mary Beth Steinbach, who was the managing editor and reviewed the final version of the book, and Candy Farrell, the electronic production specialist whose wizardry was applied to the production of the book. I express my heartfelt thanks to all of them. I especially would like to thank my wife, Lokamatha Sarma, for putting up with me while I spent many weekends working on this book.

Research Strategy

1.1 1.2 1.3 1.4 1.5 1.6

Introduction Measurement Scales for Variables Defining the Target Sources of Modeling Data Pre-ProcessIng the Data Alternative Modeling Strategies

1 ..2 ....2 .......11 12 14

1.1 Introduction This chapter discusses the planning and organization of a predictive modeling project. Planning involves tasks such as these: •

defining and measuring the target variable in accordance with the business question

•

collecting the data

•

comparing the distributions of key variables between the modeling data set and the target population to verify that the sample adequately represents the target population

•

defining sampling weights if necessary

•

performing data-cleaning tasks that need to be done prior to launching SAS Enterprise Miner

Alternative strategies for developing predictive models using Enterprise Miner are discussed at the end of this chapter.

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

1.2 Measurement Scales for Variables Because many of the steps above will involve a discussion of the data and types of variables in our data sets, it is important that I first define the measurement scales for variables that are used in this book. In general, I have tried to follow the definitions given by Alan Agresti': •

A categorical variable is one for which the measurement scale consists of a set o f categories.

•

Categorical variables for which levels (categories) do not have a natural ordering are called nominal.

•

Categorical variables that do have a natural ordering of their levels are called ordinal.

•

An interval variable is one that has numerical distances between any two levels of the scale.

According to the above definitions, the variables INCOME and AGE in Tables 1.1 to 1.5 and BAL_AFTER in Table 1.3 are interval-scaled variables. Because the variable RESP in Table 1.1 is categorical and has only two levels, it is called a binary variable. The variable LOSSFRQ in Table 1.2 is ordinal. (In Enterprise Miner you can change its measurement scale to interval, but I have left it as ordinal.) The variables PRIORPR and NXTPR in Table 1.5 are nominal. In some manuals and books, interval-scaled variables are called continuous. Continuous variables are usually treated as interval variables as a matter of practical convenience. Therefore I use the terms interval-scaled and continuous interchangeably. I also use the terms orderedpolychotomous variables and ordinal variables interchangeably. Similarly, I use the terms unordered polychotomous variables and nominal variables interchangeably.

1.3 Defining the Target The first step in any data mining project is to define and measure the target variable to be predicted by the model that emerges from your analysis of the data. This section presents examples of this step applied to five different business questions. The examples shown here are intended to highlight the fact that the tasks of defining and measuring the target variable can be nontrivial.

1.3.1 Predicting Response to Direct Mail In the example used in this book, a hypothetical auto insurance company wants to acquire customers through direct mail. The company wants to minimize mailing costs by targeting only the most responsive customers. Therefore, the company decides to use a response model. The target variable for this model will be RESP, and it is binary, taking the value of 1 for response and 0 for no response. Table 1.1 shows a simplified version of a data set used for modeling the binary target, response (RESP).

Alan Agresti, Categorical Data Analysis, 2 ed. (New York, NY: John Wiley & Sons, 2002), 2.

Chapter 1: Research Strategy

Table 1.1 CUSTOMER

AGE

INCOME

S24.000

STATUS

RESP

S45.0Q0 S

S61.000

MNC

531,000 MC

323,436 MC

Si 00.25 6 D

S345.67S

MNC

: j

S100.2M

S21.312

â&#x20AC;˘

S83.456

524 J 34

MNC

543,566

512.002 MC

S32.454

561.345

S76.123

S25.324

S31.3S6 MNC

S7S.345 S

S61.234

MNC

123

S76.S76

S24.002

In Table 1.1 the variables A G E , I N C O M E . STATUS, PC, and N C are input variables (or explanatory variables). A G E and I N C O M E are numeric and, although they could theoretically be considered continuous, it is simply more practical to treat them as interval variables. The variable S T A T U S is categorical and nominal-scaled. The categories o f this variable are S i f the customer is single and never married, M C i f married with children, M N C i f married without children, W i f widowed, and D i f divorced. The variable PC is numeric and binary. It indicates whether the customers own a personal computer or not, taking the value 1 i f they do and 0 i f not. The variable N C represents the number o f credit cards the customers own. You can decide whether this variable is ordinal or intervalscaled.

4 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

The target variable is RESP and takes the value 1 if the customer responded, for example, to a mailing campaign, and 0 otherwise. A binary target can be either numeric or character; I could have recorded a response as Y instead of 1, and a non-response as N instead o f 0, with virtually no implications for the form of the final equation. Note that there are some extreme values in the table. For example, one customer's age is recorded as 6. This is obviously a recording error, and the age should be corrected to show the actual value, i f possible. INCOME has missing values that are shown as dots, while the nominal variable STATUS has missing values that are represented by blanks. The Impute node o f Enterprise Miner can be used to impute such missing values. See Chapters 2, 6, and 7 for details.

1.3.2

Predicting Risk in the Auto Insurance Industry

The auto insurance company wants to examine its customer data and classify its customers into different risk groups. The objective is to align the premiums it is charging with the risk rates of its customers. I f high-risk customers are charged low premiums, the loss ratios will be too high and the company will be driven out of business. I f low-risk customers are charged disproportionately high rates, then the company will lose customers to its competitors. By accurately assessing the risk profiles o f its customers, the company hopes to set customers' insurance premiums at an optimum level consistent with risk. A risk model is needed to assign a risk score to each existing customer. In a risk model, loss frequency can be used as the target variable. Loss frequency is calculated as the number o f losses due to accidents per car-year, where car-year is equal to the time since the auto insurance policy went into effect, expressed in years, multiplied by the number of cars covered by the policy. Loss frequency can be treated as either a continuous (interval-scaled) variable or a discrete (ordinal) variable that classifies each customer's losses into a limited number o f bins. (See Chapters 5 and 7 for details about bins.) For purposes o f illustration, I model loss frequency as a continuous variable in Chapter 4 and as a discrete ordinal variable in Chapters 5 and 7. The loss frequency considered here is the loss arising from an accident in which the customer was "at fault," so it could also be referred to as "at-fault accident frequency." I use loss frequency, claim frequency, and accident frequency interchangeably. Table l .2 shows what the modeling data set might look like for developing a model with loss frequency as an ordinal target.

Chapter 1: Research Strategy

Table 1.2 CUSTOMER 1

AGE

INCOME

N PR MO

545.000

561.000

S24.000

S31,000

523,456

•

S100.2S6

S345.67S

5100.211

S21.312

524.23-

543.566

S12.002

S32.454

561,343

576.123

IS 19

S25J24

S31.SS6

378,345

S61.234

123

S76.S76

524.002

0 1

6 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

The target variable is LOSSFRQ, which represents the accidents per car-year incurred by a customer over a period of time. This variable will be discussed in more detail in subsequent chapters in this book. For now it is sufficient to note that it is an ordinal variable that takes on values o f 0, 1,2, and 3. The input variables are AGE, INCOME, and NPRVIO. The variable NPRVIO represents the number of previous violations a customer had before he purchased the insurance policy.

1.3.3 Predicting Rate Sensitivity of Bank Deposit Products In order to assess customers' sensitivity to an increase in the interest rate on a savings account, a bank may conduct price tests. Suppose one such test involves offering a higher rate for a fixed period of time, called the promotion window. In order to assess customer sensitivity to a rate increase, it is possible to fit three types of models to the data generated by the experiment: â&#x20AC;˘

a response model to predict the probability of response

â&#x20AC;˘

a short-term demand model to predict the expected change in deposits during the promotion period

â&#x20AC;˘

a long-term demand model to predict the increase in the level of deposits beyond the promotion period

The target variable for the response model is binary: response or no response. The target variable for the short-term demand model is the increase in savings deposits during the promotion period net o f any concomitant declines in other accounts. The target variable for the long-term demand model is the amount of the increase remaining in customers' bank accounts after the promotion period. In the case of this model, the promotion window for analysis has to be clearly defined, and only customer transactions that have occurred prior to the promotion window should be included as inputs in the modeling sample. Table l .3 shows what the data set looks like for modeling a continuous target.

If a customer increased savings deposits by $100 but decreased checking deposits by $20, then the net increase is $80. Here, net of means excluding.

Chapter h Research

Table 1.3 CUSTOMER

AGE

INCOME

B.JAN

B_FEB

B_MAR

B_APR

BAL_ AFTER

S45.000

54.000

S4.230

S4.400

54.900

55.900

S61.000

S5.G00

S4.000

S3.000

52.000

51.200

SI.100

53.000

S100

S2O0

524.000

55.234

S345

35.678

S78

S87S

S31.000

S4.000

S4.230

54.400

S4.900

54.950

S23.456

52.000

S4.000

S3.000

51,000

51.200

51.100

S3.CO0

5100

S1JO0

S100.256

S5.234

$345

55.678

S78

Sl.OSS

S345.67S

S3.435

S4.674

5678

SSO.00O

550.000

S100.211

S787

S4.230

S4.400

S4.900

55.900

S21.312

SS.7S0

57.SOO

S3.456

510.000

55.000

54.000

S3.000

S4,000

S24.234

54.000

54.230

S4.4Q0

S4.900

S5.900

S43.566

54,674

S678

SS00

S7,890

SS.S90

512,002

55,234

S345

S3.678

578

S1.07S

532.454

54,0X1

S4.230

S4.4O0

S4.900

S5.900

S61.343

S2.000

54.000

53.000

SLOOO

S76.123

SI,200

SI. 100

53.000

Sioo

51.100

S25.324

S5.234

S345

55.678

S78

SLOTS

S31.SS6

33,435

34,674

S678

ss.ooo

S9.000

S7SJ45

$787

54.230

S4.4Q0

54.900

55.900

561.2 3-i

SS.7S0

57.SOO

53,456

SIOO

123

576.876

S5.000

S4.000

S3.000

51,034

S24.0O2

S4.00Q

54.230

54.400

S4.900

S7,245

•

Strategy

8 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

The data set shown in Table 1.3 represents an attempt by a hypothetical bank to induce its customers to increase their savings deposits by increasing the interest paid to them by a predetermined number o f basis points. This increased interest rate was offered (let us assume) in May 2006. Customer deposits were then recorded at the end of May 2006 and stored in the data set shown in Table 1.3 under the variable name BAL_AFTER. The bank would like to know what type of customer is likely to increase her savings balances the most in response to a future incentive of the same amount. The target variable for this is the dollar amount of change in balances from a point before the promotion period to a point after the promotion period. The target variable is continuous. The inputs, or explanatory variables, are AGE, INCOME, B_JAN, B_FEB, B MAR, and B_APR. The variables B_JAN, B_FEB, B_MAR, and B_APR refer to customers' balances in all their accounts at the end of January, February, March, and April of 2006, respectively.

1.3.4 Predicting Customer Attrition In banking, attrition may mean a customer closing a savings account, a checking account, or an investment account. In a model to predict attrition, the target variable can be either binary or continuous. For example, i f a bank wants to identify customers who are likely to terminate their accounts at any time within a pre-defined interval of time in the future, it is possible to model attrition as a binary target. However, if the bank is interested in predicting the specific time at which the customer is likely to "attrit," then it is better to model attrition as a continuous t a r g e t â&#x20AC;&#x201D; time to attrition. In the example shown below, attrition is modeled as a binary target. When you model attrition using a binary target, you must define a performance window during which you observe the occurrence or non-occurrence of the event. I f a customer attrited during the performance window, the record will show 1 for the event and 0 otherwise. Any customer transactions (deposits, withdrawals, and transfers of funds) that are used as inputs for developing the model should take place during the period prior to the performance window. The inputs window during which the transactions are observed, the performance window during which the event is observed, and the operational lag, which is the time delay in acquiring the inputs, are discussed in detail in Chapter 7 where an attrition model is developed. Table 1.4 shows what the data set looks like for modeling customer attrition.

Chapter I ; Research Strategy

Table 1.4

| CUSTOMER

AGE

INCOME

B_JAN

B.FEB

B_MAR

B_.\PR

ATTR

545,000

34.000

S4.230

S4.400

S4.900

S61.000

35.000

S4.000

S3.000

SI.200

S1.100

S3.000

S100

S24.000

SS.234

S345

S5.67S

S78

S31.000

S4.000

S4.230

S4.4O0

S4.900

S23.456

S2.000

S4.000

S3.000

SI.200

51,100

S3.000

S100

S10O.256

S5.234

S345

S5.67S

S78

S345.678

53.433

54,674

5673

SS0 000

S1002I1

S787

54,230

S4.40Q

54.900

321.312

SS.7S0

S7.S00

S3.456

S5.000

S4.000

S3.000

S24.234

S4.000

S4^30

54.400

54.900

S43.566

S4.674

S678

SSOO

S7.S90

SI 2.002

S5J34

S345

S5.67S

S32.454

S4.000

54,230

S4.400

S4.900

S61.345

S2.000

S4.000

S3.000

S76.123

SI. 200

SI.100

S3 000

5100

S25.324

S5.234

S345

S5.67S

S78

S31.SS6

S3.435

54.674

S678

SSO.000

S7S.345

S787

54.230

54.400

54.900

S61.234

SS.7S0

S7.S00

S3.456

123

S76.S76

S5.000

54.000

S 3.000

S24.002

S4.000

S4.230

S4.4O0

S4.900

S78

In the data set shown in Table 1.4. the variable A T T R represents the customer attrition observed during the performance window, consisting o f the months o f June. July, and August o f 2006. The target variable takes the value o f ) i f a customer attrits during the performance window and 0 otherwise. Tablel.4 shows the input variables for the model. They are A G E . I N C O M E , B _ J A N . B_FEB, B M A R . and B_APR. The variables B J A N , B_FEB, B _ M A R , and B_APR refer to customers' balances for all o f their accounts at the end o f January, February. March, and A p r i l o f 2006, respectively.

Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business

Applications

1.3.5 Predicting a Nominal C a t e g o r i c a l (Unordered Polychotomous) Target Assume that a hypothetical bank wants to predict, based on the products a customer currently owns and other characteristics, which product the customer is likely to purchase next. For example, a customer may currently have a savings account and a checking account, and the bank would like to know if the customer is likely to open an investment account, or open an I R A , or take out a mortgage. The target variable for this situation is nominal. Models with nominal targets are also used by market researchers who need to understand consumer preferences for different products or brands. Chapter 6 shows some examples o f models with nominal targets. Table l .5 shows what a data set might look like for modeling a nominal categorical target. Table 1.5

CUSTOMER

AGE

INCOME

PRIORPR

NXTPR

S45.000

S61.000 B

S24.000

ÂŤ

S31.000 B

S23.456

S 100.256 A

$345,678

Si 00,211 CD

S21.312

524.234

S43.566

S12.002 E

532 ^-4 A

S6I.345

S76.123

S25J24

S31,836

5-S.345

561.254

123

576.876

S24.O02 D

Chapter 1: Research Strategy 11

In Table 1.5, the input data includes the variable PRIORPR which indicates the product or products owned by the customer of a hypothetical bank at the beginning of the performance window. The performance window, defined in the same way as in Section 1.3.4, is the time period during which a customer's purchases are observed. Given that a customer owned certain products at the beginning of the performance window, we observe the next product that the customer purchased during the performance window and indicate it by the variable NXTPR. For each customer, the value for the variable PRIORPR indicates the product that was owned by the customer at the beginning of the performance window. The letter A might stand for a savings account, B might stand for a certificate of deposit, etc. Similarly, the value for the variable NXTPR indicates the first product purchased by a customer during the performance window. For example, i f the customer owned product B at the beginning of the performance window and purchased products X and Z, in that order, during the performance window, then the variable NXTPR takes the value X. I f the customer purchased Z and X, in that order, the variable NXTPR takes the value Z, and the variable PRIORPR takes the value B on the customer's record.

1.4 Sources of Modeling Data It is important to distinguish between two different scenarios by which data becomes available for modeling. For example, consider a marketing campaign. In the first scenario, the data is based on an experiment carried out by conducting a marketing campaign on a well-designed sample o f customers drawn from the target population. In the second scenario, the data is a sample drawn from the results of a past marketing campaign and not from the target population. While the latter scenario is clearly less desirable, it is often necessary to make do with whatever data is available. In such cases, you can make some adjustments through observation weights to compensate for the lack of perfect compatibility between the modeling sample and the target population. In either case, for modeling purposes, the file with the marketing campaign results is appended to data on customer characteristics and customer transactions. Although transaction data is not always available, these tend to be key drivers for predicting the attrition event.

1.4.1 Comparability between the Sample and the Target Universe Before launching a modeling project it is necessary to verify that the sample is a good representation of the target universe. This can be done by comparing the distributions o f some key variables in the sample and the target universe. For example, i f the key characteristics are age and income, then you should compare the age and income distribution between the sample and the target universe.

1.4.2 Observation Weights I f the distributions o f key characteristics in the sample and the target population are different, sometimes observation weights are used to correct for any bias. In order to detect the difference between the target population and the sample, it is necessary to have some prior knowledge of the target population. Assuming that age and income are the key characteristics, you can derive the weights as follows: Divide income into, let's say, four groups and age into, say, three groups. Suppose that the target universe has N people in the /'* age group and / * income group, and 0

assume that the sample has n people in the same age-income group. In addition, suppose the i}

total number of people in the target population is TV, and the total number o f people in the sample

12 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

is n. In this case, the appropriate observation weight is (Ny IN) /(n^ I n) for the individual in the /'* age group and / * income group in the sample. These observation weights should be constructed and included for each record in the modeling sample prior to launching Enterprise Miner, in effect creating an additional variable in your data set. In Enterprise Miner, you assign the role of Frequency to this variable in order for the modeling tools to consider these weights in estimating the models. The situation described here inevitably arises when you do not have a scientific sample drawn from the target population, which is very often the case. However, there is another source of bias that is often deliberately introduced. This bias is due to over-sampling of rare events. For example, in response modeling, i f the response rate is very low, it is necessary to include all the responders available and only a random fraction of nonresponders. The bias introduced by such over-sampling is corrected by adjusting the predicted probabilities with prior probabilities. This technique will be discussed in Section 4.7.2.

1.5 Pre-Processing the Data Pre-processing has several purposes: •

eliminate obviously irrelevant data elements, e.g., name, social security number, street address, etc., that clearly have no effect on the target variable

•

convert the data to an appropriate measurement scale, especially converting categorical (nominal-scaled) data to interval-scaled when appropriate

•

eliminate variables with highly skewed distributions

•

eliminate inputs which are really target variables disguised as inputs

•

impute missing values

Although many cleaning tasks can be done within Enterprise Miner, as the following examples show, there are some that should be done prior to launching Enterprise Miner.

1.5.1 Data Cleaning Before Launching SAS Enterprise Miner Data vendors sometimes treat interval-scaled variables, such as birth date or income, as character variables. I f a variable such as birth date is entered as a character variable, it would be treated by Enterprise Miner as a categorical variable with many categories. To avoid such a situation, it would be better to derive a numeric variable from the character variable and then drop the original character variable from your data set. For example, in one modeling project, I first converted the birth date to SAS format, derived age from the birth date, and then used age as an input variable. Similarly, income is sometimes represented as a character variable. The character A may stand for $20K ($20,000), B for $30K, etc. To convert the income variable to an ordinal or interval scale, it is best to create a new version of the income variable in which all the values are numeric, and then eliminate the character version of income. Another situation which requires data cleaning that cannot be done within Enterprise Miner arises when the target variable is disguised as an input variable. For example, a financial institution would like to model customer attrition in its brokerage accounts. A model is to be developed to predict the probability o f attrition during a time interval of three months in the future. The institution decides to develop the model based on actual attrition during a performance window of

Chapter 1: Research Strategy

three months. The objective is to predict attritions based on customers" demographic and income profiles and balance activity in their brokerage accounts prior to the window. The binary target variable takes the value o f 1 i f the customer attrits and 0 otherwise. I f a customer's balance in his brokerage account is 0 for two consecutive months, then he is considered an atlritor. and the target value is set to I . I f the data set includes both the target variable (attrition/no attrition) and the balances during the performance window, then the account balances may be inadvertently treated as input variables. To prevent this, inputs which are really target variables disguised as input variables should be removed before launching Enterprise Miner. These examples demonstrate that while there is no uniformly applicable solution to data cleaning, every effort should be made to ensure that instances such as those illustrated above are resolved before launching Enterprise Miner.

1.5.2

Data C l e a n i n g After L a u n c h i n g S A S E n t e r p r i s e Miner

Display 1.1 shows an example o f a variable that is highly skewed. The variable is M S , which indicates the marital status o f a customer. The variable RESP represents customer response to mail. It takes the value o f 1 i f a customer responds, and 0 otherwise. In this hypothetical sample, there are only 100 customers with marital status M (married), and 2900 with S (single). None o f the married customers are responders. An unusual situation such as this may cause the marital status variable to play a much more significant role in the predictive model than is really warranted, because the model tends to infer that all the married customers were non-responders because they were married. The real reason there were no responders among them is simply that there were so few married customers in the sample. Display 1.1

The SAS System The FREQ Frequency Percent Row Pet Col Pet

Procedure

Table of MS by R E S P RESP(RESPONSE) MS(MARITAL STATUS)

Total

100 3.33 100.00 3.42

0 0.00 0.00 0.00

100 333

2B26 94.20 97.45 96.58

74 2.47 2.55 100.00

2900 96.67

2926 97.53

74 2.47

3000 100.00

Total

Variables such as the one shown in Display I . I can produce spurious results i f used in the model. You can identify these variables using the StatExplore node, set their roles to Rejected in the Input Data node, and drop them from the table using the Drop node.

14 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

The Filter node can be used for eliminating observations with extreme values, although I do not recommend elimination of observations. Correcting them or capping them instead may be a better alternative, in order to avoid introducing any bias into the model parameters. The Impute node offers a variety of methods for imputing missing values. These nodes will be discussed in detail in the next chapter. Imputing missing values is necessary when you use the Regression or Neural Network nodes.

1.6 Alternative Modeling Strategies The choice of modeling strategy depends on the modeling tool and the number o f inputs under consideration for modeling. Here are examples of two possible strategies to consider in using the Regression node.

1.6.1 Regression with a Moderate Number of Input Variables Pre-process the data: •

Eliminate obviously irrelevant variables.

•

Convert nominal-scaled inputs with too many levels to numeric interval-scaled inputs, i f appropriate.

•

Create composite variables (such as average balance in a savings account during the six months prior to a promotion campaign) from the original variables i f necessary. This can also be done with Enterprise Miner using the SAS Code node.

Next, use Enterprise Miner to perform these tasks: •

Impute missing values.

•

Transform the input variables.

•

Partition the modeling data set into train, validate, and test (when the available data is large enough) samples. Partitioning can be done prior to imputation and transformation, because Enterprise Miner automatically applies these to all parts of the data.

•

Run the Regression node with the Stepwise option.

1.6.2 Regression with a Large Number of Input Variables Pre-process the data: •

Eliminate obviously irrelevant variables.

•

Convert nominal-scaled inputs with too many levels to numeric interval-scaled inputs, i f appropriate.

•

Combine variables i f necessary.

Next, use Enterprise Miner to perform these tasks: •

Impute missing values.

•

Make a preliminary variable selection. (Note: This step is not included in Section l .6.1.)

•

Group categorical variables (collapse levels).

Chapter 1: Research Strategy 15

•

Transform interval-scaled inputs.

•

Partition the data set into train, validate, and test samples.

•

Run the Regression node with the Stepwise option.

The main difference between the steps outlined in Sections 1.6.1 and 1.6.2 is that Enterprise Miner is used to make a preliminary variable selection in Section 1.6.2 (because the number o f inputs is large) and not in Section 1.6.1. The steps given in Sections 1.6.1 and 1.6.2 are only two of many possibilities. For example, one can use the Decision Tree node to make a variable selection and create dummy variables to then use in the Regression node.

Getting Started w i t h Predictive Modeling

2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8

Introduction Opening SAS Enterprise Miner 5.2 Creating a New Project in SAS Enterprise Miner 5.2 The SAS Enterprise Miner Window Creating a SAS Data Source Creating a Process Flow Diagram Summary... Appendix to Chapter 2

17 18 20 ...21 22 35 ....72 72

2.1 Introduction This chapter introduces you to S A S Enterprise Miner 5.2 and some of the preprocessing and data cleaning tools (nodes) needed for data mining and predictive modeling projects. The following topics are covered: •

opening Enterprise Miner 5.2

•

starting a new project:

•

naming the project

providing a directory where Enterprise Miner stores the data sets and programs that it generates for the project

specifying the directory where the user's data sets are located.

creating a data source for each data set used in the project: o

providing the name and location of the data set

setting the measurement scales and roles of the input variables

creating a profit matrix for decision-making.

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

â&#x20AC;˘

using some of the nodes for data cleaning, data exploration, variable selection, and variable transformation: o

Input Data node (for specifying the data set for the project)

StatExplore node (for univariate analysis of the inputs and exploring the relationship of inputs to the target)

MultiPlot node (also for univariate analysis of the inputs and exploring the relationship of inputs to the target)

Impute node (for imputing missing values)

Data Partition node (for partitioning the data set into subsets for use in training, validating, and testing models)

Filter node (for examining and filtering outliers)

Variable Selection node (for selecting important variables for modeling)

Transform Variables node (for performing various transformations to improve model accuracy)

S A S Code node (for writing custom code)

Drop node (for dropping variables).

Note: The order in which the nodes are presented is not necessarily the order in which they should generally be used. Enterprise Miner's modeling toolsâ&#x20AC;&#x201D;Decision T r e node, e Neural Network node, and Regression nodeâ&#x20AC;&#x201D;are not included in this chapter as they are covered extensively in Chapters 4, 5, and 6.

2.2 Opening SAS Enterprise Miner 5.2 1

To start Enterprise Miner 5.2, click on the Enterprise Miner icon on your desktop . The logon screen will appear as shown in Display 2.1.

All the illustrations presented here are from sessions in which Enterprise Miner 5.2 is installed on a personal computer.

Chapter 2: Getting Started with Predictive Modeling

Display 2.1

SAS Analytics Platform

§sa& SAS' Enterprise Miner 5.2 The

ftmtr

to Know

User name:

^asadm

Password

Remember my password

Personal Workstation

Log On

Cancel

Enter your user name and password in the logon screen. Check the Personal Workstation box i f you are using Enterprise Miner 5.2 on a personal computer. When you click the Log O n button, the Enterprise Miner w i n d o w opens, as shown in Display 2.2. Display 2.2 Si Enterprise Miner fit

Edt

v*w

Actions Options Window Help W e l c o m e to Enterprise Miner

Enterprise Miner

;*S

Help Topics

'ip

New Project...

—' -

• B P

Open Project.. Recent Projects

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

2.3 C r e a t i n g a N e w P r o j e c t in SAS E n t e r p r i s e M i n e r 5 . 2 Creating a new project involves naming the project, as well as indicating the directory where you want the project to be saved and the directory where the data for the project exists. These steps are shown below. When you select New Project from the Enterprise Miner window shown in Display 2.2, the Create New Project window opens, as shown in Display 2.3. Display 2.3 Create Mew Project General | Start-Up Code j Exit Code Name: chapter2

Host:

SASMain - Logical Workspace Server

Path;

C:\TheBook'iEM 5.2Wov2006

Cancel

Help

In this window enter the Name o f the project and the Path where you want to save the project. I typed chapter2 in the Name box, and typed C: \TheBook\EM_5 . 2\Nov2 006 in the Path box. This is the directory where the project w i l l be stored. Next, I selected the Start-Up Code tab. On the Start-Up Code tab, I typed the L I B N A M E statement as shown in Display 2.4. Display 2.4 Create New Project: General Slart-Up Code | Exit Code | 1 2 3

o p t i o n s nofrnterr ; lib-name TlieBook. "C: \Tlie3ook\8ata"

;

The data for this project is located in the folder c: \TheBook\Data. This w i l l be indicated by the libref TheBook. When you select O K , the Enterprise Miner 5.2 interface window opens, showing the new project. This window is shown in Display 2.5.

t 'hapter 2: Gelling Storied with Predictive Modeling

2.4 The SAS E n t e r p r i s e M i n e r W i n d o w This is the window where you create the process flow diagram for your data mining project. In Display 2.5,1 have numbered the components o f the Enterprise Miner window for easy reference. Display 2.5 Enterprise M i r e r - C h a p t e r 2 File

Edit

View

Actions

Options

Window

Help

QIJBIH fc|

Sample] Explore j Motify | Mqd"ejJ Assess | Ulilty f

5fe a • 13

• Stiff

13 Chapter2 * Q Data Sources - J^J Diagrams

Response 9i Model Packages

Property

Value EMVys Response Available

Name Status

ID Diagram Identifier. This identifier

corresponds to the SAS libref used to identify the physical location of the contents of this diagram on the server

' * Connected to SASMain - Logical Workspace Serve'

Diagram Response opened

A brief description o f these components follows: O Menu Bar: The menu bar has seven items. These are File, Edit. View. Actions. Options. Window, and Help. By clicking on any one o f these buttons, you w i l l see a drop-dow n menu. For example, by clicking on View-MVopcrty S h e e t s Advanced, you can reset the properties list from Basic to Advanced. This is illustrated in Display 2.6. Display 2.6 Enterprise M i n e r - C h a p t e r 2 File •

Ed.t

View

Actions

Options

j Window

Property Sheet

Dii

Sample j J • * QDat * _4]Dia. •

4JM

BJUH

[*] Program Editor

Ctrl+Alt+P

Log

Ctrl+Alt+L

O Output

Ctrl+Alt+O

Graphs

Ctrl+fllc+G

Table...

Help

Ctrl+Alt+T

Refresh Project Tree Ctrl+Alt+R

Basic • Advanced Hide

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

Toolbar: The toolbar contains the Enterprise Miner node (tool) icons. The icons displayed on the toolbar change according the Node (Tool) G r o u p tab you select in the next line indicated by © in Display 2.5.

© Node (Tool) Groups: These tabs are for selecting different groups of nodes. The node groups are Sample, Explore, Modify, Model, Assess, and Utility. The toolbar © changes according to the node group selected. If you select the Sample tab on this line, you will see the icons for Input Data, Sample, Data Partition, and T i m e Series in ©. I f you select the Explore tab, you will see the icons for Association, Cluster, Multiplot, Path Analysis, S O M / K o h o n e n , StatExplore, Text Miner, and Variable Selection in ©. Similarly, other tabs can be used according to the tasks you are performing in Enterprise Miner. ©

ProjectPanel: The project panel is for viewing, creating, deleting, and modifying the Data Sources, Diagrams, Model Packages, and Users. For example, if you want to create a data source (that is, tell Enterprise Miner where your data is and give information about the variables, etc.), you click on Data Sources and proceed. For creating a new diagram, you right-click on Diagrams and proceed. To open an existing diagram, double-click on the diagram you want.

Properties Panel: The properties ofProject, Data Sources, Diagrams, Nodes, Model Packages, and Users are shown in this panel. Note that, in our example, the nodes are not yet created; hence, you do not see them in Display 2.5. Y o u can view and edit the properties of any object selected. I f you want to specify or change any options in a node such as Decision T r e e or Neural Network, you must use the Properties Panel.

Help Panel: This displays a description of the property that you select in the Properties Panel.

Status B a r : This indicates the execution status of the Enterprise Miner task.

Toolbar Shortcut Buttons: These are shortcut buttons for Create Data Source, Create Diagram, R u n , etc. To display the text name of these buttons, position the mouse pointer over the button.

Diagram Workspace:This is used for building and running the process flow diagram for the project with various nodes (tools) of Enterprise Miner.

2.5 Creating a SAS Data Source Y o u need to create a data source for each data set you use in the project. Creating a data source means providing Enterprise Miner with the relevant information about the data set being imported—the dataset's name, location, library path, the role assignments and measurement levels of its variables, and the decision variables attributed to the target such as the profit matrix, etc. The profit matrix, also referred to as Decision Weights in Enterprise Miner 5.2, is used in decisions such as assigning a target class to an observation and assessing the models. (The use of profit matrices is discussed in detail in Chapter 4.) Enterprise Miner saves all of this information, or metadata, as different data sets in a folder called DataSources in the Project Directory. To create a data source, click on the toolbar shortcut button or right-click on Data Sources in the Project Panel, as shown in Display 2.7.

Chapter 2: Getting Started with Predictive Modeling

Display 2.7 Enterprise Miner - Chapter2

g j Mj

®m

•B

ill

£33

Sample ! E>J)!ore ; Modify : Model i Assess Utility 13 :iiapter2

Diagrams f £ ] Create Data Source • • i»J Mgdd Packages =y Property

Name Project Name

I * , Connected to SASMah - Logical VNtorkspace Server

When you click on Create Data Source, the Data Source Wizard window opens, and Enterprise Miner prompts you to enter the data source. This window is shown in Display 2.8. Display 2.8

•HE)

$ Data Source Wizard - Step 1 of B Metadata Source

Select a metadata source

Source: S A S Table

Next>

Cancel

H e I i >

. I

Is the source a S A S Table or is it a Metadata Repository? If, as in my example, you are using a SAS data set in your project, enter S A S Table in the Source box and click on Next>. S A S Table is, by default, already selected in the Source box. So, you can simply click on Next>. Then another window opens, prompting you to give the location o f the SAS data set. This window is shown in Display 2.9.

Predictive Modeling with SAS Enterprise Miner: Practical Sol it! ions for Business Applications

Display 2.9

â&#x20AC;˘IS

Data Source Wizard - Step 2 of 6 Select a SAS Table

.....

Select a SAS table

Browse

Table:

< Back

)(

Next >

Cancel

[

Help

When you click Browse.., a window opens that shows the list o f library references. This window is shown in Display 2.10. Display 2.10 mm

Select a SAS Table .i) 9 J ||

MAPS SAMPSIO SASHELP THEBOOK

Cancel

Since the data for my example project is in the library TheBook, when I double-click on T H E B O O K , the window opens with a list o f all the data sets in that library. This window is shown in Display 2.11.

Chapter 2: Getting Started with Predictive Modeling

Display 2.11 Select a SAS Table NEURAL NEURALDATA MEWTAL NUMRIJ3TARG NUMRI_NTARG PRDSALE PR ICE TEST PRICETEST_SCORE RESP

RESPB RESPB1 RESPB2 RESPB3 RESP_MOD RESP.SMPL. RESP_SMPL._CL.EAN RESP_SMPL_CLEAN2 RESP_SMPLâ&#x20AC;&#x17E;CLEAN3 RISK Properties.

Cancel

I select the data set named N N _ R E S P _ D A T A . When 1 click O K , the Data Source Wizard window opens as shown in Display 2.12. Display 2.12 Data Source Wizard

- Step ? of 6 Select a SAS Table

Select a SAS table

Table:

Browse..

THEBOOK NN RESP DATA

= Back

Next>

Cancel

He*

This display shows the libref and the data set name, which I entered. When I press Next>. another window opens displaying the Table Properties. This is shown in Display 2.13.

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

Display 2.13 Y D a l a S o u r c c Wizard ~ S t e p 3 o f 6 Table Information

Table Properties Value THE BOOK NN RESP DATA

Property Table Name Description Member Type Data Set Type Engine Number ol Variables Number ol Observations Created Date wod it ted Dote

DATA DATA V9 18 29904 2005-03-11 0B:S052.015 2005-03-11 08:50:52.015

Help

Clicking on Next> opens another window that shows Metadata Advisor Options. This is shown in Display 2.14A. Display 2.14A .,Y Data Source Wizard -- Step 4 of 6 Metadata Advisor Options

Metadata Advisor Options Use trie basic setting to set (he initial measurement levels and roles based on the variable attributes. Use the advanced setting to set the Initial measurement levels and roles based on both the variable attributes and distributions.

O Basic

[

@ Advanced

-Back

|| Next*

Customize

( Cancel

| | Help j

Display 2.I4A provides a snapshot o f the Metadata Advisor Options window, which is used for defining the metadata. Metadata is data about data sets. It specifies how each variable is used in the modeling process. The metadata contains information about the role o f each variable, its measurement scale, etc. I f you select the Basic option, the initial measurement levels and roles are based on the variable attributes. This means that i f a variable is numeric, its measurement scale is designated to be interval, irrespective o f how many distinct levels the variable may have. For example, a numeric binary variable w i l l initially be given the interval scale. I f your target variable is binary in numeric form, it w i l l be treated as an interval-scaled variable, and it w i l l be treated as such in the subsequent nodes. I f the subsequent node is a Regression node. Enterprise Miner automatically

Chapter 2: Getting Started with Predictive Modeling

uses ordinary least squares regression, instead o f the logistic regression, which is usually appropriate with a binary target variable. By selecting the Basic option, all character variables w i l l be considered nominal. You may reset the measurement scales and roles as appropriate. By selecting the Advanced option. Enterprise Miner applies a bit more logic as it automatically sets the variable roles and measurement scales. I f a variable is numeric and has more than 20 distinct values, Enterprise Miner sets its measurement scale to interval. In addition, i f you select the Advanced option, you can customize the measurement scales. For example, by default the Advanced option sets the measurement scale o f any numeric variable to nominal i f it takes 20 or fewer unique values, but you can change this number by clicking the Customize button. Display 2 . I 4 B shows the default settings for the Advanced Advisor Options. Display 2.14B t-, A d v a n c e d Advisor O p t i o n s

[x]|

Prooertv Value Missing Percentage Threshold 50 RejectVars with Excessive Missing Value Yes Class Levels Count Threshold 20 DelectClass Levels Yes Relect Levels Count Threshold fob ReiectVars with Excessive Class Values Yes Class L e v e l s C o u n t T h r e s h o l d I f "Detect class levels''~Yes, interval variables w i t h less than the number specified for this property w i l l be marked as N O M I N A L . The default value is 20

]

j |

Cancel

j [

Help

One advantage o f selecting the Advanced option is that Enterprise Miner w i l l automatically set the role i f each unary variable to Rejected. I f any o f the settings are not appropriate, you can change them later in the window shown in Display 2.15. In this example, l changed the Class Levels Count Threshold property to 5 and closed the Advanced A d v i s o r Options window by clicking on O K and then clicking on Next>. This brings up the window shown in Display 2.15.

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

Display 2.15 : Data Source Wizard

-Step 5 of 6 Column Metadata Show code Explore Name APE CRED DEUNO DEPC EMP STA GENDER HEQ INCOME M.FDU MILEAGE MOB MRTGI MS NUMTR RESTYPE RES_STA cuscode resp

Role

Input Input Input Input Input Input Input Input Input Input Input Input Input Input Input Input Rejected Input

Report

Level nterval nterval Nominal binary Nominal Binary Nominal Nominal Binary nterval Binary Nominal Nominal Nominal Nominal Elinary Nominal v Binary

Order

No NO No No No No No No No No No No No No No No No No

Drop NO No No Ho Mo No No No Ho Ho NO No No No No No No NO

Lower

•

Help

In this window, a tabic is shown with the variable names, model roles, and measurement levels o f the variables in the data set. For the purpose o f this illustration, 1 specify the model role o f the variable RESP as the target, and click Next>. Enterprise Miner recognizes that the target is binary. Hence, it opens another window with the question Do you want to build models based on the values of the decisions? This window is shown in Display 2.16. Display 2.16 •jData Source Wizard ~ Step 6 of 0 Decision Crmllijut alton

Decision Processing Do von want to build models based on the values ol the decisions ? II you answer yes. you may enter Information on the cost or protS of each possible decision, prior probability and cost lunction. The data will be scanned tor the distributions ol the target variables.

< Back

Next >

Yes

Cancel

Help

I f you are using a profit matrix (decision weights), cost variables, and posterior probabilities, you should select Yes, and click Next > to enter these values. (These matrices can also be entered and modified at later stages.)

Chapter 2: Getting Started with Predictive Modeling

A window opens with tabs for Targets. Prior Probabilities. Decisions, and Decision Weights. The window for the Targets tab is shown in Display 2.17. Display 2.17 : Data Saurte Wizard â&#x20AC;&#x201D;Step 7 of 6 Decision Cniifiauraiioo Targets | prior PiobabWies [ Decisions | Decision Weights | Name :

resp

Measurement Level

Binary

Target level order:

Descending

Event level:

Format:

Help

The Targets tab displays the name o f the target variable and its measurement level. It also gives the target levels o f interest. In this example, the variable RESP is the target and is binary, which means that it has two levels, namely response, indicated by I , and non-response, indicated by 0. The event o f interest is response. That is, the model is set up to estimate the probability o f response. I f the target has more than two levels, this window w i l l show all o f its levels. In later chapters, I w i l l model an ordinal target that has more than two levels, each level indicating frequency o f losses or accidents, with 0 indicating no accidents, I indicating one accident, and 2 indicating two accidents, and so on. Display 2.18 shows the Prior Probabilities tab. Display 2.18 : Data Source Wizard â&#x20AC;&#x201D; S t e p 7 o F G

Targets Prior ProbaUiWies j Decisions | Decision Weights | Do you wont to errter new prior probabilities? F Ye* T No Level I 0

Court ... 10525

Pr-i 0.3i36 0.6854

Aci.urtfJFTi.jr 0.03 0 97

Help

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

This tab shows (in the column labeled Prior) the probabilities o f response and non-response calculated by Enterprise Miner for the sample used for model development. In the modeling sample I used in this example, the responders are over-represented. In the sample there are 31.36% responders and 68.64% non-responders. These are called prior probabilities. So the models developed from the modeling sample at hand w i l l be biased unless a correction is made for the bias caused by over-representation o f the responders. I f you enter these prior probabilities in the Adjusted Priors column as I have done above, Enterprise Miner w i l l correct the models for the bias and produce unbiased predictions. To enter these adjusted prior probabilities, select Yes in response to the question Do you want to enter new prior probabilities? Then enter the probabilities that you calculated separately for the entire population (.03 and .97 in my example). In order to enter a profit matrix, click on the Decision Weights tab. Display 2.19 shows the Decision Weights (profit matrix) window. Display 2.19 -'.[Data Source Wizard -- Step 7 of 8 Decision Configuration Targets | Prior Probabilities | Decisions Decision Weights Select a decision function: C Minimize

C MaxMxe

Eriter weiglit volues for the decisions DECISION2 | DECISION! Level jog ...|12.0 1 J 00 _j_1i,g"_

< Back

Next*

Cancel

Help

The columns o f this matrix refer to the different decisions that need to be made based on the model's predictions. In this example, Dccisionl means classifying or labeling a customer as a responder, while Decision2 means classifying a customer as a non-responder. The entries in the matrix indicate the profit or loss associated with a correct or incorrect assignment, so the matrix in this example implies that i f a customer is classified as a responder, and he is in fact a responder. then the profit is $12. I f a customer is classified as a responder, but she is in fact a non-responder, then there w i l l be a loss o f $1. Other cells o f the matrix can be interpreted similarly. In developing predictive models, Enterprise Miner assigns target levels to the records in a data set. In the case o f a response model, assigning target levels to the records means classifying each customer as a responder or non-responder. In a later step o f any modeling project, Enterprise Miner also compares different models, on the basis o f a user-supplied criterion, to select the best model. In order to have Enterprise Miner use the criterion o f profit maximization when assigning target levels to the records in a data set and when choosing among competing models, select Maximize for the option Select a decision function. The values in the matrix shown here are arbitrary, and given only for illustration. Display 2.20 shows the window for cost variables.

Chapter 2; Getting Started with Predictive Modeling 31

Display 2.20 Data Source Wizard —Step 7 of a Decision Co nfigurat ion Targets | Prior Prctoabilities Decisions | Decision Weights | Do you want to usa the decisions? C No

P Yes Decision Nomoj DECISION1 Jl DECISION2 ]6

Label

| Cost Variable Constant i«None » 00 < None > 00

Help

I f you want to maximize profit instead o f minimizing cost, then there is no need to enter cost variables. Costs are already taken into account in profits. Therefore, in this example, cost variables are not entered. When you click on Next>, another window opens showing the data set and its Role. Display 2.21 shows this window. In the display, the data set N N _ R E S P _ D A T A is being assigned a role o f Raw. Display 2.21 Data Source. Wizard --Step If of 8 Data Source Attribute*

You may change the name and the tole, and can specify a population segment identifier for the data source to be created.

Name:

|NN_RESP_DATA

Role: ^

[ROVV

" 5

Segment: (~

Noles:

Fnsh

Cancel

Help

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

Other options for Role are T r a i n , Validate, Test, Score, Document, and Transaction. Since I plan to create the T r a i n , Validate, and Test data sets from the sample data set, 1 leave its role as Raw. When 1 click on Finish, the Data Source Wizard closes, and the project window opens as shown in Display 2.22. Display 2.22

I have upgraded the Property Sheet from Basic to Advanced by clicking on View-> Property Shcet ^Advanced, as shown in Display 2.23. -

Display 2.23 _ ! • ; x; Fie Edrt View Actions Options V. Enterprise Miner - chapter? _

Window Help Baste

[*] Program Ector Qrl+Alt+P

S*3>!eJ 23 chapter

HLOQ Q Output

Orr+Afc+t Ctrl+AH+O

ffi Q D a t Graph, • J^jDiai— • yjMor Table...

Ctrl+AH+G

•*: jlJUs<

Ctrl+Afc+R

Refresh Project

Properly

if wuvair.eu

H i m ® D\D\m ,\

9 Isia

m &|

Ctrl+Aft+T

Value

f i * . Connected to SASMain - Logical Workspace Server

Display 2.24 shows Advanced properties o f the project named Chapter2.

Chapter 2: Getting Started with Predictive Modeling

Display 2.24 • Enterprise Miner-Chaplcr2 1 Fie

Edi

Swrpio *

View

Actions

E'f^e

Optmt

Window

neb

. Model - * w e « U « y

-' 1

SiH^

Q D s I a Sources • J (Wdel BaOirioe:

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Server Grid Available

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Max C o n c u r r a n l T a s k s

Default

HUM f l * , Connected io SASMan • L f f j c M Viat Jooce Serve.

In order to see the properties o f the data set, expand Data Sources in the project panel by clicking on the plus sign located to the left o f Data Sources. Then when you select the data set N N _ R E S P _ D A T A . the properties panel shows the properties o f the data source as shown in Display 2.25. You can view and edit the properties. You can also see a list o f variables by clicking on located on the right o f the Variables property in the Value column. The arrow in Display 2.25 shows where to click the mouse pointer, and Display 2.26 shows the window that opens as a result.

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

Display 2.25 ^Enterprise Miner - chapter 2 File Edit View Actions Options Window Help ° E L*3 0

•

Sample 1 Explore j Modify j Model j Assess | Utility | 3 cnapter2 - n ] Data Sources NN RESP DATA

• H Diagrams Model Packages JTJ Users Property ID Name

NNRESPDATA NN RESP DATA

Variables

Decisions

pole

Raw

JSegme.nt .External Data Sampling Rate Run Code Notes

Yes

THEBOOK NN_RESP_DATA DATA 29904.0 Tie kssarmatsasadm 10/3/05 12:46 PM sasadm 10/4/05 6:17 AM Local

Library Table Tffit No.Obs.

No. Cols.

Created By Create Date Modified By Modify Date Scope

ID Data Source identifier The metadata tables are stored in the SAS library, and use this identifier as its LIBREF. Connected to SASMain - Logical Workspace Server

Display 2.26

rttn*

Rote input

CRED DELINO

Input

DEPC

Input Input

UFPlj

rnpul

EMP__STA Input LENDER Input hiptit HEG INCOME Input

Input input 'injujl MS Input rJUMTR Input RESTYFE Input R E S . S T A Input custrjde Refected rasp Taryel MrlEAOE MOB MRTC-I

Outer

Leva I':1s IV. 11 Interval Nominal Binary Norniial Binary nominal Nominal Binary Interval Brnary

jiorntail

Nominal Nominal Nominal Binary Nominal Binary

[_ r>oc

lower U r *

UapaLmt

type

-L

IS m •i EdtlBma SAS Coo*

This window shows the name, role, measurement scale, etc., for each variable in the data set. Y o u can change the role o f any variable, change its measurement scale, or drop a variable from the data set. I f a variable is dropped from the data set, it w i l l not be available in any o f the subsequent nodes.

Chapter 2: Getting Started with Predictive Modeling

2.6 C r e a t i n g a P r o c e s s Flow Diagram To create a process flow diagram, right-click on Diagrams in the project panel (shown in Display 2.25) and click on Create Diagram. Y o u w i l l be prompted to enter the name o f the diagram in a text box labeled Diagram Name. After entering a name for your diagram, click O K . Then a blank workspace opens, as shown in Display 2.27A, where you create your process flow diagram. Display 2.27A a

ffi b

' SsmrJs • E>r*ie Mottly Uo*l Amis Lilly 3 Chr*(«2 RESPONSE Property ID

Hsme

2.6.1

vnta EMWS RESPONSE Op»n

Input Data Node

This is the first node in any diagram (unless you start with the SAS Code node). In this node, you specify the data set that y o u want to use in the diagram. There may be several data sources that you may have already created for this project, as discussed in Section 2.5. From these data sources, you need to select one for this diagram. A data set can be assigned to an input in one o f two ways: •

When you expand Data Sources in the project panel by clicking on the [+] on the left o f Data Sources, all the data sources w i l l appear. Then click on the icon to the left o f the data set you want, and drag it to the Diagram Workspace. This w i l l create the Input Data node with the desired data set assigned to it.

•

Alternatively, first drag the Input Data node from the toolbar into the Diagram Workspace. Then set the Data Source property o f the Input Data node to the name o f the data set. To do this, select the Input Data node, then click on Q

located to the right o f

the Data Source property as shown in Display 2.27B. The Select Data Source window opens, as shown in Display 2.28. Then click on the data set you want to use in the diagram. Then click O K . When you follow either procedure above, the Input Data node is created as shown in Display 2.27B.

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

Display 2.27B °3 chapter^ • J Data Sources - J^J Diagrams -S-fj Ch2_TranVar . Ch2_VarSel RESPONSE_B ^ RESPONSE J j Model Packages 3 Ai Users

• Value

Property

r-Variables rbectslons [Output Type •-Rote "Rerun

I 1 1: 1' 1

Node ID Imported Data Exported Data

•

NN_RESP_DATA • c

View Raw No

i-Data Selection '•New Table

Data Source

HData Source [-Table r Library r.Descriptjpri ;• Number of Observations

NN_RESP_DATA NtJ RESP DATA FHEBOOl

29904 18 No

Number of Columns -External Data -Sampling Rate -Segment -Scope

•

Local

i-Time of Creation i-Run id j-Last Error j-Last Status : [-Needs Updating

12/5/06 8:25 AM

Yes

Display 2.28 <| Select Data Source Name Cr>2 VarSel l')! .'.l l j . ' H TESTIMPUTE

ID CH VARSEL NMRE5PDATA — TESTIMPUTE

Table

Variables

BINARYTARGET NN_RE5P_DATA TESTIMPUTE

Cancel

Help

C 'hapter 2: Getting Started with Predictive Modeling

2.6.2 Tools f o r Initial Data Exploration To explore the data, you can use the Enterprise Miner nodes MultiPlot and StatExplore. These nodes enable you to examine the distributions o f the input variables and their relationships with the target variable. In order to use these nodes you must create a process flow diagram, as shown in Display 2.29. To include MultiPlot and StatExplore nodes in the process flow diagram, click on the Explore tab in the node groups (see Display 2.5). The exploration tools are now visible on the toolbar. When you place your mouse pointer over a specific exploration tool, the tool's name becomes visible. Drag the StatExplore and MultiPlot tools onto the Diagram Workspace and connect the nodes as shown in Display 2.29. Display 2.29

UlllPIOl

2.6.2.1 S t a t E x p l o r e N o d e The StatExplore node can be used to find out which input variables are closely related to the target. When you run the StatExplore node and open the Results window, you w i l l see a graph window and an output window. The graph window, shown in Display 2.30A, is called C h i - S q u a r c Plot. This plot shows the statistic known as Cramer's V on the vertical axis and the inputs on the horizontal axis. A Chi-Square plot is shown for categorical targets. Display 2.30A | i Chi Si uore Plot Target = tesp 0.20>

0.15¬

11; | 0.10¬ 2 t^ Ordered Inputj » i r i . n i ' i V • 1-1(114* K1TOE

u.uu-

,— 2

1 4

— r — 7 6

r ~ i i

i i — ~ i i — i i — h — i , — 1 1 — ,

8 9 10 Ordered Inputs

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

When you place the mouse pointer over a bar, you can see the variable name and the Cramer's V value for that variable. In this example, the top variable is RESTYPE. This variable indicates the customer's residence type (homeowner or renter). Cramer's V for RESTYPE is 0.165347. In general, Cramer's V measures the strength o f relationship between two categorical variables. A detailed explanation o f Cramer's V is given in the appendix to this chapter. Cramer's V is automatically generated for all categorical inputs when the target is also categorical. T o calculate Cramer's V for continuous inputs such as A G E , you must convert the variable from continuous to categorical by creating intervals, or bins, with each bin representing a category. To accomplish this, set the Interval Variables property o f the StatExplore node to Yes, and specify the Number of Bins property to the desired number o f bins, or leave it at its default value o f 5, as shown in Display 2.30B. To get a Chi-Square plot, make sure that the C h i Square property o f the StatExplore node is set to Yes. Display 2.30B Property Node ID Imported Data Exported Data Variables Use Seamen! Variables

Value Stal

r Hide Rejected Variables Yes '•Number of Selected Variables 1000 : l-Chl-Square r Interval Variables 1 "[Number.of Bins . ,

Yes Yes i5

: j-Correlations [•Pearson Correlations "Spearman Correlations

Yes Yes

[•Time of Creation : [ Run Id [• Last Error [•Last Status [Needs Updating [ Needs to Run [Time of Last Run j [ Run Duration "Grid Host

12/2/06 2:1 GPM

Eip§!9HHHHHH

fes Yes

_ _

For example, the interval-scaled variable A G E is grouped into five bins, which are 18-32.4, 32.4-46.8, 46.8-61.2, 61.2-75.6, and 75.6-90. In the appendix to this chapter, a step-by-step illustration o f the calculation o f the Chi-Square statistic and Cramer's V is shown for the variable AGE. The Chi-Square value shows the strength o f relationship between the target and the binned variable. More specifically, it shows i f the distribution o f the target levels differs significantly from bin to bin among the bins constructed from the interval variable. From the output sub-window in the Results window o f the StatExplore node, you can see the modal value o f each input for each target level. For the input RESTYPE, the modal values are shown in Output 2 . 1 .

Chapter 2: Getting Started with Predictive Modeling 39

Output 2.1 Variable-RESTYPE Target Value

Target

Huncat

Bode Pet

KHlas

_ OVERALL^ reap reap

HOHE HOHE RENTER

54.73 60.19 52.05

Mode2 RENTER REKTER HOHE

40.42 35.11 42.77

This output reports the modal values o f the inputs by the target levels. In this example, the target has two levels: 0 and 1. The columns labeled Mode Pet and Mode2Pct exhibit the first modal value and the second modal value, respectively. The first row o f the output is labeled _ O V E R A L L _ . The _ O V E R A L L _ row values for Mode and Mode Pet indicate that the most predominant category in the sample is homeowners, indicated by H O M E . The second row indicates the modal values for non-responders, and the third row shows the modal values for the responders. The first modal value for the responders is RENTER, suggesting that the renters in general are more likely to respond than home owners in this marketing campaign. These numbers can be verified by running PROC FREQ from the Program Editor, as shown in Display 2.30C. Display 2.30C f ÂŁ 3 Program Editor -

p r o c ÂŁreq data=TheBook.NN_RE5P_DATA;

c a b l e RESTYPE*RESP/nopeccent n o r o u m i s s i n g ;

run ;

The results o f PROC FREQ are shown in Output 2.2. Output 2.2 ga output l The FREQ Procedure T a b l e o r RESTYPE b y r e s p RESTYPE

resp

Frequency I Col P e t I

Total

CONDO

I I

380 I 1.65 I

186 I 1.98 I

566

COOP

I I

535 I 2.85 I

300 I 3.20 I

885

HOHE

| I

12354 I 60.19 I

4011 I 42.77 I

16365

RENTER

I I

7206 I 35.11 I

4862 I 52.05 1

12088

Total

20525

9379

29904

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

2.6.2.2 MultiPlot Node The MultiPlot node can be used for visualizing the data. Y o u can examine the distributions o f the variables and relationships among variables. You must make sure that the Property Sheet is set to Advanced. The property sheet can be set to Advanced by clicking View->Property Sheet-> Advanced. To explore the variables, click on 2

located to the right o f the Variables property. This opens a

window with all the variables, as shown in Display 2.31.

iVariables-Plot Use

Role

Defautt Default Default Default Default Default Default MILEAGE Default MOB Default Default MRTGI MS Default MUMTR Default RESTYPE Default RES_STA Default cuscode Default resp Yes

Input Input Input Input Input Input Input Input Input Input Input Input input Input Rejected Target

ame

DELINO DEPC EMPâ&#x20AC;&#x17E;STA GENDER HEQ INCOME MFDU

Level Nominal Binary Nominal Binary Nominal Nominal Binaiy Interval Binaiy Nominal Nominal Nominal Nominal Brtary Nominal Binary

Type

Label

Order

Form

h t b b N

E N b P b c c c

Explore..,

Cancel

Help

To examine the histograms, select the variable o f interest and click on Explore. Display 2.32 shows the histograms generated for the variables RESTYPE and RESP.

Chapter 2: Getting Started with Predictive Modeling

Display 2.32 explore - CMWS.Ids_DATA Ffc

View

Actions Window

•JO|4 _JDJ2< Property

is&aMrlrBi Sample Method Fetch Size Random Seed Fetched Rows

Top

Dei&u* 10000

Apply

HOME

Plot...

RENTER

CONDO

RESTYPE _ •

.ids_DATA RESTYPE HOME

x £3

8000-1

HOME HOME HOME

HOME HOME

s u. 2000-

RENTER

HOME

RENTER'

RESP

To examine the relationship between a categorical input such as RESTYPE and the target variable RESP, click the Plot button located at the bottom o f the Sample Properties pane in the Explore window shown in Display 2.32. Then the Select a C h a r t Type window appears, as shown in Display 2.33. Display 2.33 Select a Chart Type

§9 Area Bar Histogram C P *

Bar c h a r t provides s t a t i s t i c a l r e l a t i o n s between c a t e g o r i c a l values. Bar can be subdivided and/or grouped u s i n g subgroup v a r i a b l e and/or group v a r i a b l e r e s p e c t i v e l y .

I * Scalier ! 3 Tables sk Matrix Sr| Lattice Parallel Axis h 3D Charts

FWsh

Select the bar chart and click on Next>. The Select C h a r t Roles window w i l l open, as shown in Display 2.34.

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

Display 2.34 Select Chart Roles

Response Category

RESTYPE

Type Numeric Character

I Description RESP RESTYPE

Response statistic: Cared

«Back

By clicking in the box under Role, you get a list o f roles that can be assigned to the variables listed in the first column o f the window shown in Display 2.34. In this example, I set the role o f the variables RESP and RESTYPE to Response and Category, respectively. I set the Response statistic to Mean, clicked Next> four times, and then clicked on Finish. The resulting chart is shown in Display 2.35. Display 2.35 j • - Explore - EMWS.lds_.DATA FJe Edit

View

Actions Window

| | ' Graph3 0.50-

0.40-

S0.30

£o.20 RESTYPE 0.10-

RESP(Hean)

RENTER

-0.414891

0.00

HOME

RENTER

CONDO

COOP

RESTYPE

By passing the mouse pointer over the bars, you can see the response rate for a given level o f the categorical input. From Display 2.35, you can see that the response rate is 4 1 % among renters. Y o u can also examine the relationship o f the inputs to the target variables by setting the Type of C h a r t property o f the MultiPlot node to Bar Charts, and then running the MultiPlot node. By opening the results, you can see charts such as the one shown in Display 2.36.

Chapter 2: Getting Started with Predictive Modeling

Display 2.36

R E S T Y P E by resp

F R E Q U E N C Y

56000 16000 14000 13X0 1C000 EOO0

>S36E

eoco

4000 2000 Ol

555

CONDO

CCOP

HOME

RENTER

RESTYPE resp

The examples given above demonstrate some o f the capabilities o f MultiPlot and StatExplore. For more details, see the Enterprise Miner help.

2.6.3

Impute Node

The Impute node is used for imputing missing values o f inputs. This node icon, along with other icons o f the Modify group, appear on the toolbar when you select the Modify tab o f the Enterprise Miner window (see Display 2.5). Display 2.37 shows the Impute node selected.

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

Display 2.37 j enterprise Miner - chapter? He

Lrk

View

Actions

Opt it™

Window

Help

si«sz •

S « r , * Explore I M o d t y | MoOW j A i i e a i j L U t y | rSiEsinfuiE

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N N R E S P DAT.

Data Partition

' • D e i s m Ta/jet Method: rw i'Delouri h p u M e t h r x I K t e o i w i | D e l a u l TBget Methoflone [•AEWTimg

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Default Input Method

Specifies the imputation method for interval input variables Didisjini RESf 0 ( HE Onyi t - J

h i \ C o m e d W t o SAEMam -Locngal W a k i p a c e Server

The Impute node has a number o f techniques for imputation. To select a particular technique, you must first select the Impute node on the Diagram Workspace and set the Default Input Method property to the desired technique. For interval variables, the options available are Mean, Median, Mid-Range, Distribution, Tree, Tree Surrogate, M i d - M i n i m u m space, Tukey's Biweight, Huber, Andrew's wave, Default constant, and None. For class variables, the options are Count, Default constant value, Distribution, Tree, Tree surrogate, and None.

2.6.4

Data Partition Node

In model building it is necessary to partition the sample into Training, Validation, and Test sub-samples. The training data is used for developing the model using tools such as Regression, Decision Tree, and Neural Network. During the training process, these tools generate a number o f models. The validation data set is used to evaluate these models, and then to select the best one. The process o f selecting the best model is often referred to as fine tuning. The test data set is used for an independent assessment o f the final model. The Data Partition node is shown in Display 2.38.

Chapter 2: Getting Started with Predictive Modeling 4 5

Display 2.38 j Enterprise Miner - chapter2 He

Edt

V*w Acuoru Conors

Wndow Hetn

S n S â&#x20AC;˘ â&#x20AC;˘ [_3 ill Sarrptol EiMnej t-toaty] Moddj Assess| UUtyj

In the Data Partition node, you can specify the method o f partitioning by setting the Partitioning Method property to one o f four values: Default, Simple random. Cluster and Stratified. In the case o f a binaiy target such as response, the stratified sampling method results in uniform proportions o f responders in each o f the partitioned data sets. Hence, I set the Partitioning Method property to Stratified, which is the default for binaiy targets. The default proportion o f records allocated to these three data sets are 40%. 30%. and 30%, respectively. These proportions can be changed by resetting the Training. Validation, and Test properties under the Data Set Percentage property. In order to reset the properties, you should first drag the Data Partition node into the Diagram Workspace, and select it, so that the Property Panel w i l l show the properties o f the Data Partition node as shown in Display 2.38.

2.6.5 Filter Node The Filter node can be used for eliminating observations with extreme values (outliers) in the variables. You should not use this node routinely to eliminate outliers. While it may be reasonable to eliminate some outliers for very large data sets for predictive models, the outliers often have interesting information that leads to insights about the data and customer behavior. Before using this node, you should first find out the source o f any extreme value. I f the extreme value is due to an error, the error should be corrected. I f there is no error, you can truncate the value so that the extreme value does not have an undue influence on the model. Display 2.39 shows the flow diagram with the Filter node.

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

Display 2.39

•

NN RESP DATA

•

*»

Data Partition

•

Filter

•

In the process flow shown in Display 2.39. the Filter node follows the Data Partition node. Alternatively, you can use the Filter node before the Data Partition node. To use the Filter node, select it. and set the Default Filtering Method property for Interval variables to one o f the values listed there as shown in Display 2.40A. Display 2.40A Mean Absolute Deviation (MAD) User-Specified Limits Metadata Limits

Extreme Percentiles Modal Center Standard Deviations from the Mean None

For class variables, the Default Filtering Method property can be set to one o f the values as shown in Display 2.40B. Display 2.40B iRare Values (Count) Rare Values (Percentage) JNone

Display 2.40C shows the properties panel o f the Filter node.

Chapter 2: Getting Started with Predictive Modeling

Display 2.40C - 1 — ' Property Nods ID Imported Dala Exported Data Exncrt Table TaGles to Filter Create score code

Vatue Filler TJ

Filtered All Dala Sets

Yes

-C!ass Variables ;• Default Filtering Method Rare Values (Percentage) Yes j-Keep Missing Values Yes [-NormalizedValues F Minimum Frequency Cutoff 1 [ Minimum CutoffforPereentacO 01 Maximum Number of Levels '25 ;

interval Variables Default Filtering Method Exlreme Percentiles Keep Missing Values "rfes •Cutofffor MAD •CutofTPercentiles for ExtiemtO 5 -CutoffforModal Center 9.0 -Cutoff for standard Deviation 3.0

[ Time of Creation I-Run Id iLast Error [-Last Status j-Needs Updating [•Needs to Run

12(6706 4:29 PM c8E3aac6-93a6-48aa-b58tH Complete No No

I set the Tables to Filter property to All Data Sets so that outliers are filtered in all three data sets—training,validation, and t e s t — a n dthen ran the Filter node. The Results window displays the number o f observations eliminated due to outliers o f the variables. Output 2.3 A (from the output o f the Results window) shows the number o f records excluded from the T r a i n , Validate, and Test data sets due to outliers. Output 2.3A Numben o f O b s e r v a t i o n s Table TRAIN VALIDATE TEST

Data

Included

11960 8972 8972

11557 8658 8673

Excluded 403 314 299

Outputs 2.3 to 2.5 show summary statistics for original and filtered data for the variable A G E .

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

Output 2.3 j

D a t a Role=TRAIN Variable=AGE Statiscics Number H i s s i n g Minimum Maximum He an Std D e v i a t i o n Skeuness Kurtosis

Original

Filtered

0.0000 18.0000 90.0000 49.6672 15.7092 0.1867 -0.70S8

0.0000 22.0000 88.0000 49.6075 15.3838 0.1598 -0.7781

[

.„.,.,.„—,—

Output 2.4 Data Role"VALIDATE Variable =AGE

Statistics Number Hissing Minimum Maximum He an Std Deviation Skeuness Kurtosis

Original

Filtered

0.0000 18.0000 90.0000 49.1291 15.4426 0.2047 -0.6851

0.0000

22.0000 88.0000 49.0687 15.1197 0.1799 -0.7676

j [ | | \ 1

Output 2.5 t

Data Role=TEST Variable=AGE Statistics Number H i s s i n g Minimum Haximum He an Std D e v i a t i o n Sketmess Kurtosis

2.6.6

Original

Filtered

0.0000 18.0000 90.0000 48.9826 15.4225 0.2069 -0.7138

0.0000 22.0000 88.0000 49.0687 15.1197 0.1799 -0.7676

Variable Selection Node

The Variable Selection node can be used to make a preliminary selection of the variables to be included in predictive modeling. There are a number of alternative methods with various options for selecting variables. The methods of variable selection depend on the measurement scales of the inputs and the targets. These options are discussed in detail in Chapter 3. In this chapter, I present a brief introduction to the techniques used by the Variable Selection node for different types of targets, and I show how to set the properties of the Variable Selection node for choosing an appropriate technique. There are two basic techniques used by the Variable Selection node. They are the R-Square selection method and the Chi-Square selection method. Both of these techniques select variables

C 'hapter 2: Getting Started with Predictive Modeling

based on the strength o f their relationship with the target variable. For interval targets, only the R-Square selection method is available. For binaiy targets, both the R-Square and Chi-Square selection methods are available.

2.6.6.1 R - S q u a r e S e l e c t i o n Method To use the R-Square selection method, you must set the Target Model property to R-Square. as shown in the Display 2.41. Display 2.41 Property Node ID Imported Data Variables Max Class Level Max Missing Percentage Target Model Hide Rejected Variables

Value Varsel Mi

100 50 R-Square Default

Reject Unused Variables ! [-Number of Bins I [-Maximum Pass Number i -Minimum Chi-Square

Chi-Square R and Chi Square None

PSSESSSSS239I

[-Maximum Variable Number | [-Minimum R-Square : [-Stop R-Square j [-Use AOV15 Variables i [-Use Group Variables -Use Interactions j jSPDS ! [-Last Error j [-Last Status i [-Needs Updating j ['Needs to Run j [-Time of Last Run -Run Duration ;

0.0050 1 0E-5 Yes r-es

to Yes

Complete Mo No I0J3/D5 10:39 PM

OHr.QMin.11.31 Sec.

When the R-Square selection method is used, the variable selection is done in two steps. In Step 1, the Variable Selection node computes an R-Square value (the squared correlation coefficient) based on the relationship between the target and each input variable, and then assigns the Rejected role to those variables that have a value less than the minimum R-Square. The default minimum R-Square cut-off is set to 0.005. You can change the value o f the minimum RSquare by setting the Minimum R-Square property to a value other than the default. The R-Square value (or the squared correlation coefficient) is the proportion o f variation in the target variable explained by a single input variable, ignoring the effect o f other input variables. In Step 2, the Variable Selection node performs a forward stepwise regression to evaluate the variables chosen in the first step. Those variables that have a stepwise R-Square improvement less than the cut-off criterion have the role o f rejected. The default cut-off for R-Square improvement is set to 0.0005. This can be changed by setting the Stop R-Square property to a different value.

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

For interval variables, the R-Square value used in Step 1 is calculated directly by means of a linear regression of the target variable on the interval variable, assessing only the linear relation between the interval variable and the target. To detect nonlinear relations, the Variable Selection node creates binned variables from each interval variable. The binned variables are called A O V 1 6 variables. Each A O V 1 6 variable has a maximum of 16 intervals of equal width. The A O V 1 6 variable is treated as a class variable. A one-way analysis of variance is performed to calculate the R-Square between an A O V 1 6 variable and the target. These A O V 1 6 variables are included in Step 1 and Step 2 of the selection process. Some of the interval variables may be selected both in their original form and in their binned ( A O V 1 6 ) form. If you set the Use A O V 1 6 Variables property to Y e s , then only A O V 1 6 variables appear in the Variable Selection pane of the Results window, and these will be passed on to the next node, with their Role set to Input. For variable selection, the number of categories of a nominal categorical variable can be reduced by combining categories that have similar distribution of the target levels. To use this option, set the Use G r o u p Variables property to Yes.

2.6.6.2 Chi-Square Selection Method When you select the Chi-Square selection method, the Variable Selection node creates a tree based on Chi-Square maximization. The Variable Selection node first bins interval variables and then uses the binned variable rather than the original inputs in building the tree. The default number of bins is 50, but the number can be changed by setting the Number of Bins property to a different value. Any split with a Chi-Square below the specified threshold is rejected. The default value for the Chi-Square threshold is 3.84. This number can be changed by setting the Minimum Chi-Square property to the desired level. The inputs that give the best splits are included in the final tree, and passed to the next node with the role of Input. A l l other inputs are given the role of Rejected.

2.6.6.3 Variable Selection Node: An Example with R-Square Selection A s a means of illustrating how our results depend on the settings of the properties of the Variable Selection node, I experiment with a small set of variables and show you the results below. Display 2.42 shows the process flow with the Variable Selection node.

Display 2.42

Ch2_VarSel

Data Partition

Variable Selection

Regression

Display 2.43 shows the Variables window of the Variable Selection node. The data set is partitioned such that 50% is allocated to training, 30% to validation, and 20% to test.

Chapter 2: Getting Started with Predictive Modeling

Display 2.43 ,-',! Variables - Varsel

Name •AR1N VAR20 VAR3N VAR4C VAR50 VAR6C VAR7C V-AR80 credscore resp

Use Default Default Default Default Default Default Default Default Default Yes

Role

Level

input input Input Rejected Input Rejected Rejected Input Input Target

Interval Nominal Nominal Nominal Nominal Nominal Nominal Nominal Interval Binary

Order

Type

Label

N N N C

Format

USAGE WEALTH PRC CLUSTER Credit score

fcqi've.

Cancel

Hefc

Since the target variable is binary in this example, any o f the above options shown in Display 2.44 could be used. I chose the R-Square selection method by setting the Target Model property to R-Squarc. Display 2.44 1

Value

Jfroperty ^ Target Model Hide Rejected Variables

R-Square

Reject Unused Variables •dJiiLUUJ,!. I S i

R-Square

Default M Chi-Square

[Number of Bins [-Maximum Pass Number ill*-.

R and Chi Square

None

Next, I select the threshold values for the Minimum R-Square and Stop R-Square properties. The Minimum R-Square property is used in Step 1, as described in Section 2.6.6.1. The Stop R-Square property is used in Step 2, which is also described in Section 2.6.6.1. In this example, the Minimum R-Square property is set to the default value, and the Stop R-Square property to 0.00001. The Stop R-Square property is deliberately set at a very low level so that most o f the variables selected in the first step w i l l also be selected in the second step. In other words, I effectively by-passed the second step so that all or most o f the variables selected in Step 1 are passed to the Tree node or Regression node that follows. I did this because both the Decision Tree and Regression nodes make their own selection o f variables, and I wanted the second step o f variable selection to be done in these nodes rather than in the Variable Selection node. Display 2.45 [•Maximum Variable NumfcCOOO [-Minimum R-Square 0.0050 [-Stop R-Square [-Use AOV16 Variables [ Use Group Variables -Use Interactions SPDS

1 .OE-5 Yes Yes No Yes

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

From Display 2.45, you can see that 1 set the Use A O V 1 6 Variables property to Yes. I f I chose No. the Variable Selection node (i.e., the underlying PROC D M I N E ) would still create the A O V 1 6 variables and include them in Step 1, but not in Step 2. In addition, i f you select No, these variables w i l l not be passed to the next node. When you set the Use Group Variables property to Yes, the categories o f the class variables are combined, and new variables with the prefix o f G_ are created. (This is explained in more detail in the next chapter.) With the settings shown in Display 2.45,1 ran the Variable Selection node, and after the run was completed I opened the Results window by selecting the Variable Selection node, right-clicking on it. and then clicking on Results. Display 2.46 shows the Results window. Display 2.46

The bar chart in the top left quadrant o f Display 2.46 shows the variables selected in Step 2. The bottom left quadrant shows the variables selected in Step I . The output shown in the top right quadrant shows the variables selected in Step 2. The output pane in the bottom right quadrant o f the Results window shows the variables selected in Step 1 and Step 2. By scrolling down the output pane, you first see the variables selected in Step 1. These also appear in Output 2.6.

Chapter 2: Getting Started with Predictive Modeling 53

Output 2.6 The DHINE Procedure R-Squares f o r T a r g e t V a r i a b l e : resp Effect

R-Square 0.010908 0.010724 0.009589 0.009517 0.009424 0.009410

C l a s s : VAR80

17 5 8 3 8 4 6

Group: A0V16: Var: A0V16: Var:

2 14 1 3 1

Class: Group: Class: Group: Class: Group:

VAR3N VAR3N VAR50 VAR50 VAR20 VAR20 VAR80 VAR1N VAR1N credscore credscore

0.008450 0.008392 0.006479 0.003508 0.000289 0.000162

R2 < HIHR2 R2 < HIKR2 R2 < HINR2

;

Scrolling down further in the output pane, you can see the variables selected in Step 2. These are shown in Output 2.7.

Output 2.7 TTÂťe DHIHE Procedure Effects Chosen for Target: resp

Effect

R-Square

F Value

p-Value

Sun of Squares

Group: VAR3N A0V16: VAR1N Group: VARS0 Group: VAR20 Group: VAR80

5 14 3 4 2

0.010724 0.003912 0.001891 0.000673 0.000567

19.369933 2.529776 5.715847 1.525318 2.574017

<.0001 0.0013 0.0007 0.1918 0.0763

8.628783 3.14790S 1.521686 0.541304 0.456572

Error He an Square 0.089095 0.088882 0.088741 0.088720 0.088689 i

The variables shown in Output 2.7 were selected in both Step 1 and Step 2. The variables selected by the Variable Selection node are given the role of Input and passed to the next node. The Variable Selection area in the top right quadrant of the Results window (shown in Display 2.46) shows the variables with their assigned roles. The next node in the process flow is the Regression node. By opening the Variables window of the Regression node, you can see the variables passed to it by the Variable Selection node. These variables are shown in Display 2.47.

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

Display 2.47 w

Variables - Reg

Name A0V16_VAR1N G_VAR"5O G~VAR3N G_VAR50 G_VARBO

resp

Use Default Default Default Default Default Yes

*1 Report No No No No No No

! Role .Input â&#x20AC;˘Input

Input Input

jnput, Target,

Level Ordinal Nominal Nominal Nominal Nominal Binary

I Type N

Order

Lab

1 Explore...

Cancel

Help

Display 2.48 (taken from the output area o f the Results window) shows that V A R I N is rejected in Step I , but A 0 V 1 6 _ V A R 1 N is selected in both Step 1 and Step 2, indicting a nonlinear relationship between V A R 1 N and the target. The definitions o f the A O V 1 6 variables and the grouped class variables (G variables) are included in the SAS code generated by the Variable Selection node. This code can be accessed by selecting the Variable Selection node, right-clicking on it, and clicking on Results-^ View->Scoring->SAS Code. Display 2.48 shows a segment o f the SAS code.

Chapter 2: Getting Started with Predictive Modeling

Display 2.48 1 2 3

*** Begin Scoci n,g Code from PROC DMIHE rwifirii J!HtJtr«tmUH«UtJtHti.

4 5 6 7

l e n g t h JJARN . S 4; l a b e l _WARH_ = "WdiTiing's"; JJARH_ = ' '

8 9

10 11

12 13 j 14 • 1 5

1 6

17 18 19 20 21 22 23 24 25 | 26 27 . 28 | 29 30 31 j 32 33

34 35 36 37 j 38 39

i f (VAR1N = else i f (VAR1N <= else i f (VAR1N <= else i f (VAR1N <= el3e i f (VAR1N <= else i f (VAR1H <= else i f (VAR1N <= else i f (VAR1N <= el3e i f (VAR1N <= else i f (VAR1N <= else i f (VAR1N <= else i f [VAR1H <= el3e| i f (VAR1N <= else i f {VAR1N <= else i f (VAR1N <= else i f (VAR1N <=

) t h e n A0V16_VAR1N = 3; 3 . 1)125) t h e n A0V16_VAR1M =

10 .625) t h e n A0V16_VAR1N • 2 ; 15 .4315] t h e n A0V16_VAR1H = J; 20 .25) t h e n A0V16_VAR1N = 4; 25 .0625) t h e n A0V16_VAR1N - 5; 29 .875) t h e n A0V16_VAR1H - r>; 34 6875) t h e n A0V16_VAR1N = i; 39 5) t h e n A0V16_VAR1N = 1 44 3125) t h e n A0V16_VAR1N = 9; 49 .125) t h e n A0V16_VAR1M = 10; 53 9375) t h e n A0V16_VAR1N • 1 1 ; 58 75) t h e n A0V16_VAR1N = 12; 63 5625) t h e n A0V16_VAR1H = 13;

375) t h e n A0V16_VAR1M

« 14;

73 1875) t h e n A0V16_VAR1K = 15;

'6VZ ^B|dsiQ ui UMoqs si ssjqBUBA aq; j o aouBjjodiui 9Ai;Bj9i a q x 'saiqeuBA p s j o a p s a q i a i u o o s q gqj ui pasn sajqeuBA a q j puB

'99.11jo

adAj Q I V H D

sjonnsuoo j s u p / j a s u d i a j u g

o j Ajjsdojd p p o j \ i ; 3 3 J B X s q j J9S 'uouaujo s j B n b s - i q o

uou,oa|as ajenbs-mo

MJIM

a j E n b g - i q ^

Suisn u o ; j 0 9 | 9 S 9 | q B U B A u u q r j s d o j ,

9 q j

a|diuexg uv :apoN uojioaias aiqeueA

P'9'9'Z

rptra ' = NCOTA~9 ( J s

NCOTA~3 t J ( ,

= N£HVA~9

H„)iraittt H£HVA~£I ( i i *s = MeWA~3 ( . = KCHVA~9 ( ,

1 j

N£OTA~9 ( i I ! I \

1 I j

= N£HVA~9

N£tfVA~9 'T N£tfVA~3 _ = H£HVA 3 iX ?T = N £ H V A ~ 9 fZ s N £ H Y A ~ 3 _ = K£HVA 3 N£HVA~9 i

(, ( i ( , ( « (, ( * ( i ( ,,

9„)uaqn

Z„)xxavia.

= N£HVA~£) ( i = N£HVA~9

( J

.'(ZTIMOff k o a l a s ( ZTHttOlT )<ZDQKUOBtt NCHVA )^nd = 2THH0N

•'2T J ZTHb!0N~ usfiusT

(penuiiuoo)

suotjaatjddy ssdutsnq uof suotptjog JDOIJODUJ

uamy^ dsududiug SVS

%yz

AeidsjQ

Ht! Suippojv

d\tjDipduj

9c,

Chapter 2: Getting Started with Predictive Modeling

Display 2.49 Variable Importance

1.0 -

0.8u c n

o 0.6 Q.

E 0.4rr

0.2-

0.0

VAR BO

VARI1N

VAR 11

/AR50

VAR20

Variable

Display 2.50 shows the variables selected by the Chi-Square selection process. Display 2.50 if IB Variable Selection Name credscore /AR1N V-AR20 V-AR3N •AR4C VAR50 •AR6C VAR7C •AREO

Role Rejected Input Input Input Rejected Input Rejected Rejected Input

Lev.:-! Interval Interval Nominal Nominal Nominal Nominal Nominal Nominal Nominal

Type N N N N C N C C N

Label

Credit score

USAGE WEALTH PR. CLUSTER

Comment Var s el SmeJI...

•

2.6.6.5 S a v i n g the S A S C o d e G e n e r a t e d by the V a r i a b l e S e l e c t i o n N o d e I f the Target Model property is set to R-Square. the Use A O V 1 6 Variables property is set to Yes, and the Use G r o u p Variables property is set to Yes. then the Variable Selection node w i l l generate SAS code with definitions o f the A O V l 6 and Group variables. When you right-click on the Variable Selection node and then click on Results, the Results window opens. I f you click View S A S Results ^Flow Code in the Results window you can view the SAS code. A partial listing o f this code is shown in Display 2.51. -

58 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

Display 2.51 \ *** Begin Scoeing Code tvou

PROC DHIHE

; length _UARN_ « 4; l a b e l _OARN_ «* "Warnings"; ; UARN = ' '; i f (VARIH » .) then A0V16_VAR1N - 3; ' el3e i f (VAR1N <- 5.8125) then A0V16_VAR1H »1; ;;else i f (VAR1N <= 10.625) then A0V16_VAR1H »2; ;;else | ; i f (VARIH <= 15.4375) then A0V16_VAR1N = 3; : else ! \ i f (VARIH <= 20.25) then A0V16_VAR1H »4; i else [ i f (VAR1N <- 25.0625) then A0V16_VAR1H - 5;

•Seise i f (VARIH <«29.875) then A0V16_VAR1H »6; ; else | i f (VARIH <= 34.6875) then A0V16_VAR1N «• 7; j else) | i f (VARUS <= 39.5) then A0V16_VAR1N = 8; s else [ i f (VARIH <»44.3125) then A0V16_VAR1N = 9; i else j i f (VAR1H <- 49.125) then A0V16_VAR1H - 10; i else i i f (VAR1N <»53.9375) then A0V16_VAR1H " JUL; | else \ i f (VARlil <= 58.75) then A0V16_VAR1H = 12; J else j; i f (YAR1H <= 63.5625) then A0V16_VAR1H = 13; ; else ! i f (VARIH <»68.375) then A0V16_VAR1N = 14; \ else [ i f (VARIH <- 73.1875) then A0V16_VAR1N - 15; e l s e A0V16_VAR1H = 16;

This can be saved by clicking File-^Save as and giving a name to the file where the SAS code is being saved and pointing to the directory where the file is being saved. The code can also be saved by clicking Results View Scoring SAS Code, followed by File->Save as.

2.6.6.6 The Procedures Behind the Variable Selection Node Enterprise Miner uses P R O C D M I N E in variable selection. Prior to running this procedure, it creates a data mining database ( D M D B ) and a data mining database catalog ( D M D B C A T ) using P R O C DMDB. In the Results window of the Variable Selection node, you can see the log by clicking on View on the menu bar and selecting log. Display 2.52 shows the log from P R O C DMDB.

Chapter 2: Getting Started with Predictive Modeling 59

Display 2.52 Run proc dmdb w i t h the s p e c i f i e d maxlevel c r i t e r i o n .

* EH: DHDBClass Macro ;

%macro DHDBClass; VAR20(ASC) VAR3N(ASC) VAR50(ASC) VAR80(ASC) resp(DESC) %mend DHDBClass;

* .

* EH: DHDBVar Hacro ;

» .

%macro DHDBVar; VARIH credscore *mend DHDBVar;

*•

* EH: Create DHDB;

libname _ s p d s l i b SPDE "C:\DOfJTJHE~lNsasadm\LOCALS~l\Temp\SAS Temporary Files\_TD4908" L i b r e f _SPDSLIB was s u c c e s s f u l l y assigned a s f o l l o w s : Engine: SPDE P h y s i c a l Name: C:\D0COHE~l\sasadm\L0CALS-l\Temp\SAS Temporary Files\_TD4908\ proc dmdb b a t c h data°EHErsl.Part_TRAIN dmdbcat=H0RK.EH_DHDB maxlevel ° 101 o u t » _ s p d s l i b . EH_DHDB r

c l a s s %DHDBClass; var %DHDBVar; target resp run;

L Display 2.53 shows the

log from PROC DMINE.

Display 2.53 * Varsel: Input Variables Hocno ; %macco INPUTS; VARIH VAR20 VAR3N VAR50 VAR80 CREDSCORE %mend INPUTS; proc dnine data=_spd31ib.EH_DHDB dadbcacHORK.EH_DHDB Binr2»0.G05 aaxtowsOOOO stopr.2°0.00001HOINTER USEGROUPS outesc^aORK._Varsel_0UTESTDHINE j] H0H0HTT0R PSHORT var %INPUTS; target resp; , code £ile-'X:\TheBoo^t\EH_S.2\Nov2006^ChapceE2B\«or!lspoces\ErTHSl\VaE3el\EHr^.0HSC0RE.3a3*; code Cile='X:\TheBoo>t\EH_S.2\Nov2006\Chapt4:E2B\Hoi^pace3\Eira31\Vttr3el\EHPUBLISHSC0RE.3a3" cun;

2.6.7 Transform Variables Node Sections 2.6.7.1 and 2.6.7.2 describe the different transformation methods available in the Enterprise Miner Transform Variables node. Sections 2.6.7.3 to 2.6.7.6 show some examples of how to make the transformations, save the code, and pass the transformed variables to the next node.

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

2.6.7.1 Transformations for Interval Inputs â&#x20AC;˘

Simple Transformations The available simple transformations are Log, Square Root, Inverse, Square, Exponential, and Standardize. They can be applied to any interval-scaled input. These simple transformations can be used irrespective of whether the target is categorical or continuous.

â&#x20AC;˘

Binning Transformations In Enterprise Miner, there are three ways of binning an interval-scaled variable. To use these as default transformations, select the Transform Variables node, and set the value

of the Interval Inputs property to Bucket, Quantile, or Optimal in the Default Methods section. o

Bucket: The Bucket option creates buckets by dividing the input into n equal-sized intervals and grouping the observations into the n buckets. The resulting number of observations in each bucket may differ from bucket to bucket. For example if A G E is divided into the four intervals 0-25,25-50, 50-75, and 75-100 then the number of observations in the interval 0-25 (bin 1) may be 100, the number of observations in the interval 25-50 (bin 2) may be 2000, the number of observations in the interval 50-75 (bin 3) may be 1000, and the number of observations in the interval 75-100 (bin 4) may be 200.

Quantile: The Quantile option groups the observations into quantiles (bins) with an equal number of observations in each. If there are 20 quantiles, then each quantile consists of 5% of the observations.

Optimal Binning for Relationship to Target: This transformation is available for binary targets only. The input is split into a number of bins, and the splits are placed so as to make the distribution of the target levels (for example, response and nonresponse) in each bin significantly different from the distribution in the other bins. To help you understand optimal binning on the basis of relationship to target, consider two possible ways of binning. The first method involves binning the input variable recursively. Suppose you have an interval input X that takes on values ranging from 0 to 320. A new variable with 64 levels can be created very simply by dividing this range into 64 sub-intervals as follows:

0 < X < 5 , 5<X<10,

,310<^<315, 3 1 5 < X < 3 2 0 .

The values the new variable can take are 5, 10, 15... 315, which can be called splitting values. Y o u can split the data into two parts at each splitting value and determine the Chi-Square value from a contingency table, with columns representing the splitting value of the input X and rows representing the levels of the target variable. Suppose the splitting value tested is 105. At that point, the contingency table will look like the following:

*<105

X>105

"12

Target level 1 0

Chapter 2: Getting Started with Predictive Modeling

where, n ,n ,n , and n denote the number of records in each cell of the contingency table. The computation of the Chi-Square statistic is detailed in Chapter 4. n

The splitting value that gives the maximum Chi-Square determines the first split of the data into two parts. Then each part is split further into two parts using the same procedure, giving two more splitting values and four partitions. The process continues until no more partitioning is possible. That is, there is no splitting value that gives a Chi-Square above the specified threshold. At the end of the process, the splitting values chosen define the optimal bins. This is illustrated in Display 2.54.

Display 2.54 Optimal binning of an interval input

All records (5.000)

2.CCD records

3.000 records

Jf < 25

1,000 records

'â&#x20AC;˘

X i. 25

500 records

2,000 records

1,500 records

From Display 2.54, the optimal bins created for the input X are X < 25, 25 < X < 105, 105 < X < 135 and 135 < X . Four optimal bins are created from the initial 64 bins of equal size. The optimal bins are defined by selecting the best split value at each partition. The second method involves starting with the 64 or so initial bins and collapsing them to a smaller number of bins by combining adjacent bins that have similar distribution of the target levels. Enterprise Miner does not follow the steps outlined above exactly, but Enterprise Miner's method of finding optimal bins is essentially what is described in the second method above. There are other ways of finding the optimal bins. Note that in the Transform Variables node the optimal bins are created for one intervalscaled variable at a time in contrast to the Decision Tree node where the bins are created with many inputs of different measurement scales, interval as well as categorical.

â&#x20AC;˘

Best Power Transformations The Transform Variables node selects the best power transformations from among X,\og(X),sqrt(X), e \ X \ X, and X , where X is the input. There are four criteria of "best" available: u

Maximum Normal: T o find the transformation that maximizes normality, sample quantiles from each of the transformations listed above are compared with the theoretical quantiles of a normal distribution. The transformation that yields quantiles that are closest to the normal distribution is chosen.

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

Suppose Y is obtained by applying one of the above transformations to X. For example, the 0.75-sample quantile of the transformed variable Y is that value of Y at or below which 75% of the observations in the data set fall. The 0.75-quantile for a standard normal distribution is 0.6745 given by P{Z < 0.6745) = 0 . 7 5 , where Z is a normal random variable with mean 0 and standard deviation 1. The 0.75-sample quantile for Y is compared with 0.6745, and similarly the other quantiles are compared with the corresponding quantiles of the standard normal distribution. o

Maximum Correlation: This is available only for continuous targets. The transformation that yields the highest linear correlation with the target is chosen.

Equalize Spread with Target Levels: This method requires a class target. The method first calculates variance of a given transformed variable within each target class. Then for each transformation it calculates the variances of these variances. It chooses the transformation that yields the smallest variance of the variances.

Optimal Maximum Equalize Spread with Target Level: This method requires a class target. It chooses the method that equalizes spread with the target.

2.6.7.2 Transformations of Class Inputs For class inputs, two types of transformations are available. â&#x20AC;˘

Group Rare Levels transformation: This transformation combines the rare levels into a separate group, _ O T H E R _ . To define a rare level, you define a cutoff value using the Cutoff Value property.

â&#x20AC;˘

Dummy Indicators Transformation: This transformation creates a dummy indicator variable (0 or 1) for each level of the class variable.

To choose one of these available transformations, select the Transform Variables node and set the value of the Class Inputs property to the desired transformation. By setting the Class Inputs property to Dummy Indicators Transformation, as mentioned before, you will get a dummy variable for each category of the class input. Y o u can test the significance of these dummy variables using the Regression node. The groups that correspond to the dummy variables, which are not statistically significant, can be combined, or omitted from the regression. I f they are omitted from the regression, it implies their effects are captured in the intercept term.

2.6.7.3 Transformations of Targets In the Transform Variables node of Enterprise Miner 5.2, the target variables can also be transformed by setting the Interval Targets and Class Targets properties. I f you click the Value column in the Interval Targets property or Class Targets property, a list of all available transformations drops down and lets you select the desired property.

2.6.7.4 Selecting Default Methods To select a default method for transforming interval inputs, select the Transform Variables node in the Diagram Workspace and set the Interval Inputs property (under the Default Methods section in the properties panel) to one of the transformations described in 2.6.7.1. The selected method will be applied to all interval variables. Similarly, set the Class Inputs property to one of

Chapter 2: Getting Started with Predictive Modeling

the methods described in 2.6.7.2. Enterprise Miner applies the chosen default method to all inputs o f the same type. 2.6.7.5 O v e r r i d i n g the Default M e t h o d s I f you do not want to use the default method o f transformation for a particular input, you can override it by right-ciicking on 0

located on the right side o f the Variables property o f the

Transform Variables node, as shown in Display 2.55. This opens the Variables window as shown in Display 2.56. Display 2.55 3&Mer2

D ' -

rvedei Com [arisen

—

CM TwVn

CfflFira:r

'tendinis

- H e m r»g«

r-CUss " . : = Norn

TOP

•KeflKid -S:;

Maul

-fisr.dcmSsed

ii a n

Bj -CuWVJlue

ftt Ui»r Ss.-r

UrssrqVau

Md1 * * i w i V*a to M H r w x YM Cffss) I'ilui

SjrrMryVsrstlfs

Transformed a n d t a » V

Display 2.56 , Variables - Trans Mame

Number of Sins

Method

Role

Level

Log

4 Input

Interval

resp

4Target

Binary

4 Input

interval

Ordinal

«R1N

Default Optimal

VAR 2 0

nverse

VAR3N

Square

4 Input 4|nput

VAR4C

Exponential

4 Rejected

Nominal

VAR 5 0

Centering

4 inpul

Nominal

4 Rejected

Nominal

4 Rejected

Nominal

4 input

Nominal

VAR6C VAR7C

Standardize Riirlf<>t

WVR80

Quanlile

DUU.IV C I

• • •'

Order

Type

credscore

Interval

b V

I OK

Cancel

Help

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

In the Variables window, click on the Method column for the variable V A R 1 N , and select Optimal. Similarly, select the log transformation for the variable CREDSCORE (Credit score). After these changes are made, the resulting Variables window is shown in Display 2.63, which also indicates that Optimal binning is used for the variable V A R 1 N and log is used for the variable CREDSCORE. Transformations are not performed for other variables since the default methods are set to N O N E in the properties panel (see Display 2.57). Display 2.57 ,V,|Variables - Trans Name

Method

VAR1N VAR 20 ]/AR3N </AR4C VAR 50 VAR6C VAR7C VARSO credscote resp

Number ol Bins

Role 4 Input 4 Input 4jlnput 4 Rejected 4 Inpul 4 Rejected 4 Rejected 4 input 4 Input 4iTarqet

Optimal

Default Default Default Default Default Defaull Default

Low

Default

Level Interval Ordinal interval Nominal Nominal Nominal Nominal Nominal Interval Binary

Type

Order

IN |N N

N C C N N N

i (

1 Explore.

Cancel

Help

2.6.7.6 S a v i n g the S A S C o d e G e n e r a t e d by t h e T r a n s f o r m V a r i a b l e s N o d e Right-click on the Transform Variables node and run it. Then click on Results. In the Results window, click View->SAS Results-^Flow Code to see the SAS code, as shown in Display 2.58. Display 2.58 ||

Flow Code 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

* Computed Code; *

* TPJJJSFOPH: c r e d s c o r e , l o y ( c t e d s c o r e + 1 ) ; label

LOG_credscore =

'Transforced:

Credit

score';

i f c r e d s c o r e + 1 > 0 t h e n L0G_cr;edscoi:e = l o g ( c r e d s c o r e e l 3 e LOG_credscoce = .; * TPATISFORH: VARIH , Optimal B i n r i i i i g i - l ) ; l a b e l 0PT_VAR1H = 'Transformed l e n g t h 0PT_VAR1N 5 2 1 ; i f (VARIH < 13. â&#x20AC;˘>) t h e n QPT_VAR1H = "01:low

-13. S,

VMW;

mSSING";

else i f (VARIH >= 13.1) t h e n 0PT_VAR1H = " 0 2 : 1 3 . 5 - h i $ h " ;

This code can be saved by clicking FiIe->Save as. and typing in the path o f the file and the filename.

Chapter 2: Getting Started with Predictive Modeling

I f you want to pass both the original and transformed variables to the next node, you must change the Hide and Reject properties to No in the Original Variables area in the properties panel and then run the Transform Variables node. I f you open the Variables property in the next node, which is the S A S Code node in this case, you w i l l see both the original and transformed variables passed to it from the Transform Variables node, as shown in Display 2.59. Display 2.59 V a r i a b l e s - EMCODE

Name

Report

Use

Role

Level

credscore

Yes

Input

Interval

LOG_credscore

Yes

Input

Interval

0PT_VAR1N

Yes

Input

Nomina!

resp

Yes

Target

Binary

VAR1N

Yes

Input

interval

VAR 2 0

Yes

Input

Ordinal

VAR3N

Yes

l n

VAR4C

P t Rejected

Nominal

VAR 5 0 VAR6C

res No

Type

Order Crt â&#x20AC;˘

rra

Interval

input

Nominal

^Rejected

'Nominal

WE, i

Explore...

Cancel

Help

2 . 6 . 8 SAS C o d e N o d e The S A S Code node is used to incorporate SAS procedures and external SAS code into the process flow o f a project. In addition, you can perform D A T A step programming in this node. The S A S Code node is the starting point for creating custom nodes and extending the functionality o f Enterprise Miner 5.2. The S A S Code node can be used at any position in the sequence o f nodes in the process flow. For example, in Chapters 6 and 7. custom programming is done in the S A S Code node to create special lift charts. The following sections explore the S A S Code node so you can become familiar with the macro variables and macros used internally. The macro variables refer to imported and exported data sets, libraries, etc. First, look at a simple process flow with the S A S Code node, as shown in Display 2.60. Display 2.60 I

En3 Ch2_TranVar

Data Partition

10 Transform Variables

SAS C o d e

Regression

Display 2.61 shows the variables contained in the data sets passed by the Transform Variables node to the S A S Code node.

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

Display 2.61 iVarlables-EMCODE

Use Name Yes credscore LOG credscore yes iOPT VAR IN fes. fes resp VAR1N Yes Yes VAR20 VAR3N Yes VAR4C No Yes MR50 No ^AR6C Mo VAR7C Yes vAR80

Reporl

No No No

Ho No

"""NO

No No No No No No

„

Type

Level

Rote

Input Input Input Target Input .Input Rejected Input Rejected Rejected input.

Interval Interval Nominal Binary interval Ordinal interval Nominal Nominal Nominal Nominal Nominal

Order

N N

Label

Credit score Transformed: Credit score TransformedVARIN

c N

N N N C

USAGE WEALTH PROW CLUSTER

Explore... ||

OK |

Cancel

Help

To open the SAS Code window, select the SAS Code node in the Diagram Workspace and click on [I]

located to the right o f the SAS Code property in the properties panel, as shown

Display 2.62. Display 2.62 Properly \

N o d e ID

Value EMCODE ...

ilmported Data Exported D a t a Variables ; rToolType

Utility

: rData Needed

r-Rerun ••Advisor Type

•ilSSwflHflfli

Basic

;-SAS C o d e

j - C o d e Location

_ Internal File

—

;-External File '•[Catalog Entry 1 hScore Code TjScore Code Location

Internal File

1 External File i [-Catalog Entry |-Publish Code ! - Code Format ;

11 Muse Priors l-Time of Creation

Publish Datastep |Yes

11/15/06 10:50 PM

; - R u n Id i r-Last Error '•• L a s t Status - N e e d s Updating :

i r N e e d s to R u n h T i m e of L a s t R u n

No Yes

• " R u n Duration 1

"jOrjd H o s t '

•

-I .

The SAS Code window is shown in Display 2.63.

Chapter 2: Getting Started with Predictive Modeling

Display 2.63 fttSAS Code ]

Macro Variable

Qirient Value

General

hEM_lMPORT_PATA &E M_LI B. Tr a n s_TRAI N System macro variables. [ â&#x20AC;˘ E M_l M P 0 RT_DATA__C M ETA &EM_UB.Trans_CMeta_TRAIN ! EMJMPO REVALIDATE KEM LI B.. Tra n S~~VAL 1 DATE rEM_IMPORT_VAUDATE CMETAfcEM LIB.Trans CMeta TRAIN i - | {Macro Variables ^r> j Macros pi* J

The SAS Code window has three tabs: SAS Code, Macro Variables, and Macros. These tabs can be used to place the macro variables, macros, and SAS code in different locations o f the SAS Code window. Display 2.64 shows the Macro Variables and the Macros tabs placed at the top and bottom o f the SAS Code window.

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

Display 2.64

Current Value

Macro Variable

EMLIB

sasadm kssarma S561 EMWS2

i-EM. USERID H=M, METAHOST i-EM. METAPORT i;EM LIB '•Em PSEP I-EM. CQDEBAR I-EM. VERSION IEM. T00LT7PE I i-EM. NODEID

d Macro variable identifying the workspace library (e g EMWS.)

lUTY EMCODE

Macio Variable:

SAS Code

EM.DECDATA

I !-EM_REG13TER r-EM_REPORT ;-EM_DATA2CODE j EMJ3ECDATA i;EM_CHECKMACRO i'EM_CHECKSETINIT !EM_ODSLISTON ••EM ODSLISTOFF

Macro used to copy decision data set to WORK and assign the proper data set type (PROFITA-OSSyREVENUE). The arguments are NODElD-node identifier, D E C D A T A - decision data set, D E C M E T A - decision meta data set

Macros ; ; t:

In Display 2.64, i f you highlight the macro variable E M J d B , its description appears in the right panel o f the window. By scrolling down inside the Macro Variables tab, you can see the macro variables identifying the data sets imported from the predecessor nodes, as shown in Display 2.65. Display 2.65

Macro VeiiaUa

Currari Value

B : • E M_IMPO RT_DATA I-EM IMPORT DATA CM ETA I-EMJMPO RT_VAU DATE I-EM IMPORT VALIDATE CM ETA l-'EM IMPORT TEST r E M_l MP 0 RT_TE ST_C M ETA i-E'.'_IMPORT_SCORE I EM IMPORT SCORE CMETA

6EM SEM SEM SEM SEM SEM

E M _ I MPO RT_D ATA

d UB Trans UB,Trans LIB Trans UB Trans UB Trans LIB Trans

Macro variable

TRAIN CMela TRAIN VALIDATE CMeia TRAIN TEST CMela TRAIN

identifying the training data set.

Macro V i i i i b l e s

In Display 2.65, the macro variable E M _ I M P O R T J D A T A is highlighted. Its current value is & E M _ L I B . T r a n s _ T R A I N . The first part o f the current value is the macro variable & E M _ L I B whose value is E M W S 2 (see Display 2.64), which is the libref The second part is T r a n s , which indicates that the data set is created by the Transform Variables node. The third part is T R A I N , which means this data set is the training data set. From the description given in the right panel o f

Chapter 2: Getting Started with Predictive Modeling

Display 2.65, it is clear that the highlighted macro variable refers to the training data set. By scrolling down farther, you can see the macro names identifying exported data sets, files, number o f variables, and code statements. Display 2.66 shows some sample SAS code. In this code, I created a composite variable and included it in the exported data set. Display 2.66 - ilata

6en_expor.t_tr.ain; s e t sem_impor.t_data ; t o t a l _ t r . a n = var3n + v a r l n ; l a b e l t o t a l _ t t a n = Tot*2 Transactions

ran

Created

in SAS Code node"

;

a SAS Code

111 the sample code shown in Display 2.67. I create a new variable called total_tran. which is the sum o f Var3N and Var I N . Var I N and Var3N represent the number o f withdrawals and deposits from a bank per month, respectively. The composite variable total_tran represents total transactions, and it captures the customer's overall activity. The next node after the S A S Code node in the process flow is the Regression node. I f you select the Regression node, and click on [7].

located to the right o f the Variables property, all the

variables including the newly created variable total_tran appear as shown in Display 2.67. Before you do this, you must update the path. Display

2.67

... Variables-Reg2 Name

port

LOG credscore OPT VARIN VARUM V*R20 VAR3N VAR4C VAR 50 VAR6C VAR7C VAR 80 credscore fesp total (ran

X] Role Input It) put Input Input Input Rejected Input Rejectad__ Rejected input Input Target input

Level nterval Nominal nterval Ordinal nterval Nominal Nominal Nominal Nominal Nominal Interval Binary Interval

Type N

N C

C N N W N

Order

Label Transformed: Credit score Transformed VARi N

USAGE WEALTH PROXY CLUSTER Credit score Total Transactions Created in SAS Code node

_il Explore..

Cancel

Help

The S A S Code node can be used with code located in an external file. To use an external tile, select the S A S Code node and set the Code Location property to External File, and set the External File property to the full path o f the IIIe and the filename. This is shown in Display 2.68.

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

Display 2.68

Value

Property EMCODE2

N o d e ID Imported Dala Exported Data

•

.. J

Variables

• •

•iToolType

Utility

• Data N e e d e d

[No

•Rerun

iNo Basic

-Advisor Tvoe

-;SAS C o d e -Code Location

External File

- External File

C:\TheBook\EM

5.2lProgramsU_TV.SAS

- C a t a l o g Entry -Score Code Score C o d e L o c a t i o n

Internal File

-External File - C a t a l o g Enlry -Publish Code

Publish

-Code Format

Datastep

• U s e Priors

Yes

• T i m e of C r e a t i o n

11/16/06 3:01 PM

R u n id .Last Error - L a s t Status -Needs Updating •Needslo Run

Yes

- T i m e of L a s t R u n -Run Duration -Grid H o s t

2.6.9

Drop Node

The Drop node can be used for dropping variables from the data set or metadata. After an initial exploration o f your data, you can drop any irrelevant variable in this node by setting the Drop from Tables property to Yes. This setting w i l l result in dropping the selected variables from the table that is to be exported to the next node. Display 2.69 shows the settings o f the Drop from Tables property.

Chapter 2: Getting Started with Predictive Modeling

Display 2.69 Property

Value Drop

N o d e ID I m p o r t e d Data Exported Data Variables j- Drop f r o m T a b l e s

Yes

[•Assess

INo

[- C l a s s i f i c a t i o n

INo

K ^

! [-Frequency

INo

[hpdden

Yes

r- Input

:No

[•Predict

iNo

1* R e j e c t e d

Yes

[•Residual

[-Tarqet

[No

-Other

[•Time of C r e a t i o n

1 1 / 1 5 / 0 6 10:50 PM

;

[ R u n Id [•Last Error [-Last Status [•Needs Updating

Yes

[ • N e e d s to R u n

Yes

[ • T i m e of L a s t R u n [•Run Duration i =-!Grid H o s t Drop f r o m Tables I n d i c a t e s i f t h e s e l e c t e d variables s h o u l d b e d r o p p e d f r o m t h e c r e a t e d tables i n a d d i t i o n t o d r o p p i n g t h e m f r o m t h e metadata. I f set t o Y e s , t h e n t h e n o d e w i l l c r e a t e data sets i n s t e a d o f v i e w s . I f this p r o p e r t y is set t o N o , t h e n t h e v a r i a b l e s a r e o n l y d r o p p e d f r o m t h e e x p o r t e d metadata.

To select the variables to be dropped, click on ___ , located to the right o f the Variables property o f the Drop node. I f you want to drop any variable, click in the Drop column next to that variable and select Yes, as shown in Display 2.70. Display 2.70 __J Mime credscore LOO_credscore OPT_VARiN resp VAR1N VARIO VAR3N VAR50 VAR6C •AR7C VAR BO

Drop Default Default Default Dirfautl Default Del-* DetauB NO lefault Default Default <l

Role Input iroui Input Target input Input Input Rejected Input Rejected Rejected Input

L-.-veH Interval Interval Nominal Binary Interval Ordinal Interval Nominal Nominal Nominal Nominal Nominal

. Type N N C N

Order

Label Credit score Transformed: Transformed

IN USAGE WEALTH PRC CLUSTER

jc N

1 Expk*e

Format

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

2.6.10 Decision Tree Node The Decision Tree node is discussed extensively in Chapters 4 and 7 using several examples. In these chapters, the Decision Tree node is used to develop models with binary and continuous targets.

2.6.11 Neural Network Node This node is covered in Chapters 5 and 7, where I develop models for predicting response and risk for an automobile insurance company, plus price sensitivity and attrition for a hypothetical bank.

2.6.12 Regression Node This node is discussed in Chapters 6 and 7. Models are developed for binary and continuous targets.

2.6.13 Model Comparison Node You can use the Model Comparison node to evaluate a model or to compare models using your test data set. Alternative models to predict risk and response are evaluated in Chapter 7 using the Model Comparison node. Chapter 7 includes a detailed discussion of lift charts and capture rates in the training, validation, and test data sets for each model compared. I f you want to evaluate a model or compare models using the test data, you should use the Model Comparison node.

2.7 Summary •

This chapter introduced you to Enterprise Miner 5.2.

•

After reading this chapter, you should be able to open Enterprise Miner 5.2, define a new project, and create data sources for the project.

•

The chapter gave an overview of the tools (nodes) that are available for structuring and customizing data mining tasks. It provided information to help decide which tools to use and which values to assign to the various properties of the tools in order to perform data cleaning and exploration tasks for the data mining project.

•

Some of the tools mentioned here are covered in depth in subsequent chapters.

•

There are several other tools in Enterprise Miner 5.2 such as Cluster node, Principal Components node, and User Interface node, which are not covered in this book. Y o u can find information on these in the S A S Enterprise Miner help and documentation.

2.8 Appendix to Chapter 2 2.8.1 Cramer's V Cramer's V measures the strength of the relationship between two categorical variables. Suppose you have a categorical target Y with K distinct c a t e g o r i e s ^ , Y , and a categorical input K

Chapter 2: Getting Started with Predictive Modeling

X with M distinct categories X , X , ....X }

. If there are n observations in the /'* category in v

and j' category in 7 , then you can make a contingency table of the following type:

X\Y

Row Total

".2 n

•• n

Column Total

To calculate Cramer's V , you must first calculate the Chi-Square statistics under the null hypothesis that there is no association between X and Y. M

X- = Yl}""

j )

, where E =n ¥

Cramer's V =

, i=\,...M

and j = \,..K.

, which takes a value between 0 and 1 for all tables larger

/Vmin(A/-l,/:-l) than 2x2. For a 2x2 table, Cramer's V takes a value between -1 and +1. Suppose that T i s a binary variable with two levels, and X is a 16-Ievel categorical variable. Then Cramer's V = J ^ - .

2.8.2 Calculation of Chi-Square Statistic a n d Cramer's V f o r a Continuous Input As pointed out in Section 2.6.2.1, the A G E variable is divided into five groups (bins). Table 2.1 shows the number of responders ( R E S P = 1) and non-responders ( R E S P = 0).

Table 2.1 RESP Age Group 18-32.4 32.4-46.8 46.8-61.2 61.2-75.6 75.6 - 90 Overall resp rate

3830 4952 6915 3882 946 20525 0.686363

1959 2251 3181 1597 391 9379 0.313637

Total 5789 7203 10096 5479 1337 29904

Table 2.2 shows the first step in calculating the Chi-Square value. It shows the expected number of observations in each cell under the null hypothesis of independence between rows and columns.

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

Table 2.2 RESP Age Group 18-32.4 32.4-46.8 46.8-61.2 61.2-75.6 75.6 - 90

Total

3973 4944 6930 3761 918

1816 2259 3166 1718 419

5789 7203 10096 5479 1337

The expected number of observations is obtained by applying the overall response rate to each row total given in Table 2.1. 2

(O â&#x20AC;&#x201D; ES , where O h the observed frequency as

The Chi-Square for each cell is calculated as

E shown in Table 2.1, and E is the expected frequency (under the null hypothesis) as given in Table 2.1. The Chi-Square values for each cell are shown in Table 2.3.

Table 2.3 RESP Age Group 18-32.4 32.4 - 46.8 46.8-61.2 61.2-75.6 75.6 - 90

0.0000

1.0000

5.1722 0.0134 0.0304 3.9202 0.8748

11.3187 0.0292 0.0666 8.5789 1.9143

These cell-specific Chi-Square values can be retrieved from the Results window of the StatExplore node by clicking View->Summary Statistics-^Cell in the menu bar. These cellspecific Chi-Square values are shown in Display 2.71.

Display 2.71 Target resp resp [resp jresp resp resp resp resp resp resp

| AGE AGE |AGE AGE AGE AGE AGE AGE AGE AGE

Input

\ Target For... (Input Forma.I Frequency... {target Num...| trtptft Nume...|j CM Square f Log Chi Squ 3830:

18:32.4 32.4:46.8 46.8:61.2 61.275.6 75.6:90 18.32.4 32.4:46.8 46.8:612 61.275.6 75.6:80

0 J i 49S2 Z,.f o fZZZM 691& ' 6 47: 3882* *_* f\_ ' 0 62: 946* 76; 0 1959 _ 1 '18 2251 " " i ; " 3 3 : 3181 " "47 " Z.Z7A 1597 _ ~ 6 2 Ji " 391 '* r 76: 8

By summing the cell Chi-Squares, you get the overall Chi-Square {x )

5.172157} 0.030431 3;920158 0.874759 11.31875; 0.029237! 0.066592 8!578872! 1514323]

1.64329; ^431549! ~-3.49234j 1^366132} -0.13381 r 2.42646: -353232 i

-2.709171 2149302j 0549364

of31.9864 for the A G E

variable. Since there are 29904 observations in the sample, Cramer's V can be calculated

| ~i 29904

which is equal to 0.03267.

By selecting the Chi-Square Plot (or clicking in it), and selecting the Table in the Results window, you can see the Chi-Square and Cramer's V for all variables. A partial view of this table is shown in Display 2.72.

Chapter 2: Getting Started with Predictive Modeling 75

Display 2.72 R e s u l t s - 5t a t E x p l o r e

File

Edit

View

•Jo]

Window

]_!*!

Chi-Square Plot - EMWS,5tol_CHTMEJ

SEGMENTID

T=.rget

[OVERALL.

resp

_OVERALL_ _OVERALL_ _OVERALL_ OVERALL_ .OVERALL. _OVERALL_ _OVERALL_ _OVERALL_ _OVERALL_ ^OVERALL, _OVERALL_ LOVERALL, _OVERALL_ _OVERALL_ OVERALL

resp

resp resp resp resp resp resp resp resp resp resp resp resp resp

Input RESTYPE MFDU NUMTR MRTGI

Cramer's V

Chi-Square

Prob

0.165349

817.5806

= 0001

0.162414

788.8147

<00Q1

0.13795

569.0762

<.0001

0.112498

378.4571

<.0001

INCOME

0.06085

110.7257

<0001

AGE DELINQ CRED

0.032671

31.9186

<.0001

0.03132

29.3346

0.0001

0.027085

21.9140

<.0001

EMP_STA

0.022864

15.6326

0.0004

MILEAGE

0.020033

12.0011

0.0173

HEQ

0.019453

11.3215

0.0789

RES_STA DEPC GENDER MS

0.016075

7.7274

0.0054

0.011413

3.8952

0.0484

0.008698

2.2624

0.1325

0.007163

1.5366

0.4638

MOB

0.002769

0.2294

0 6320

^ 3 ^ 1 3 ^ 1 0

3 0 1 0 0 1 1 0 1 1

3.1

|||1|^|||1011011

3.2

^31*131116 8

3.3

7|^1118|01|11181！011

3.4

3 0 1 1 1 1 1 1 8 1 7 誦隱 ^ 霸 ' ^ ― ― ― ^ … " 画漏漏， … ^ 漏園 … 屬 … " 棚纏 " ^

1 0 #

3 1 1 1 1

^ ^

霸 0

^ 1

隱

^ 1

纖 0

^ 1

聽 1

…

… ^

" 龜

漏 ^

7 7 8

^ 8 1 * 1 & ― . . . . . . ^ 霸 ^ 漏 " … … " 編 ^ 漏 " … 9 9 1 0 9 1 1 0

3.5

3 . 1

1111^01111011011 丁 1115 0卜3|316『^111 ^1X111131*126 乂00

1卜6 5八5 601617)1*156 1^4106！"

&1)1& 86160(1011 &1\6

1^308『0『111 ^31*131)168 0 0 ^ 6 5 5 \^111011 31*6 655601131『0『51111(1111^ ^6^1011^ 86160(1011 00(16 15 0561\11

丫011

1^06^\5^ 丁116 ^31*13516

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： 6X^5(7；》

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卩 7乂二

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5 . 8

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0 ^ 1116

01^1(11113出 1 1 6 1 ^ 0 ^ 1010 (！！ 1^61-6111； 1&乂6『5 311(1 (！！^^!!! 11111(8 ^1111111 63011 1&乂6『 6

議 1 ( 6 5 II：乂6！7 06乂11316，八乂617 11116^ 1 0 出 6 625^

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，丁卜 6 0017)51113(1011 311(1 30(1^3(1011 111110(10113 111出6 111(1(1611 1^6^ 6)61116111:3 0 ^ 1116 0^1^141^6 ^

出 6 131*^61

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丫011 3 ^ 6 0 1 ^ 1116 31*0)11(60(111-6 6 ^ 36(11113 1116八1X11116(^111*6 |3『0|3611^ 0『1卜6 ^ 6 0 1 * 3 1 ^ 6 ^ 0 1 * 1 ^ 0『10 0 0 6 0『1116 1)11111-111 31*0111(60(111*6 ^ ( ^ ( ^ 。！ ^ .

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0『3 116111*31 116^01*1^

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3 131*^6 0001136『0『|3016111131 116111*211 0 6 1 \ ^ 0 ^ 勤 & ^ . ^

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1 ^ 8 1 ^ 1 " ！ (！^！！！！&^^!

：

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232

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1^118？5\^ (^。！！!!^ 126(1 ^3(1131 8 3 5 1 3 ？11001:1011 ^1111 5^11^1(11:118 30(1 0 0 6 0 | 0 & I ！^！^乜）

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^ 1 1 ^ 1 ^1(11113 & 0 3 ^ ^ ！ ^ ^ )

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肇丁 116 1*65口0056 3 0 ( 1 10 511011 &

1116^ 31*6 0011313(6111： ^1111 60011011110 31\6 81&1\51\01)16017，

1 1 0 ^ 10

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^6111*31 ^ 6 1 ^ 0 1 * 1 ^ 110(1& 10径6061^6

八

誦

1 0

0 1 3 1 5 1 ^ 3 5.33，

018(313乂

1(1 1113118116

5.34，

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。！！印量树 5 311(1

5.35

5 1 1 0 ^ 出 6 5 八 5 00^6『0『031011131108 ^ 6 1 ^

5.33 ；1 111)2 '(：： \7500^X^1

5， 2\^2006\^

2 ^ * 2 ^ 3

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11132 ^ 0 1 1 5 ^ 1 8 1 ^ 6 1 1 1 ^ 1 1 ^ 6 一1即01：、7811(1&仁6 ；

361：

1：1111；^ 一 — — ― ― ― . . . . . — ― ― ― ― ― ― ― ― ― ― ― ― ― ―

11132，015」131^丽一&3乜

1：1111；

3&!:& ^ 6 3 ； 361： 111)2^ 0 1 1 5 一 1 ： 1 3 1 ^ 腿 - " 3

；

！)—1033^11(10 0一1。33亡1：31 ！ ) — 1 0 3 3 ^ : 3 2 1 0 3 3 ^ 1 ： ^ 6X020仁63—13^1：3；

^66^

6X^)6(^6(1 一

0，口一1033^0十1*^1033^1(31十2*^1033^(52

11111 ； 1)100

301：乜^1:^=6(33；

13？ ^630611(11X19 6 X ^ ) 6 0 ^ 6 ^ 1 ^ 1 ( 3 ； 2111X1 ； ^1：00 3(^1 110^1： 111^； 36160^

(！^!^ ；

00101乜0

11X10 ： XXVI ^！！0111

；

^1088？！^

1116

0^1006^ 5： 1^614^1 ！^6^0^ ^0^618

10 ^^6(1x01 ^63^01186 611161尺^ 233

018^137 5 , 3 4

001131^ 0 ；

1：6仁&111 36伫6(13

；

00X1110

《1/2(0*61171

00101^

口 / ^ " & 1 1 ^ 1

^11611 ^ 6 0 = 5 ； 6 1 3 6 ；

^11611 ^ 6 0 = 1 0

《 3 / 2 0 》食 & 1 1 7 1 ^ 6 0 = 1 5

；6 1 3 6

001111^

《4/201171

001111^

( ∼ ！ ) ）食賺 1

1*611 6 6 0 = 2 5

； 6196

001111^

(6/20^*611V1

^ 6 1 1 1160=30

； 6136

±1

0 0 1 ^

《 7 / 2 0 1 ^ 6 1 1

^60=35； 6 1 3 6

±1

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(^(^&！！^丄比611

00101^

^ 1 0 / 2 0 ^ ^&11^1

001111&

《12/20)161^1

；6 1 3 6

^60=45 ； 6136

^11611

( " / ^ " 食礙丄 ^ 1 ) 6 1 1

；6 1 3 6

01611 ^ 6 0 = 2 0

0160=50

6136

360=55

6136

^ 6 1 1 ^60=60

( " " ( ！ ) 糞 6 抑 1

±1

《14/201貪膽1

^ 6 ) 1 ^60=70

±1

〔 1 5 / 2 0 " 賺 1

^ 6 1 1 ^ 。 7 3^

，6136 1

6136 6136

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117/205 ^ & ^ 1 比 6 0

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6136

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^60^90

6136

0011110

(

^ 6 1 1 660=95

6136

±1

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±1 ±1 ±1 ^60

^ 6 1 1 ^60=65

譜

6136

100 ；

11111 ； ^2：00 I&60X13 01&33

(！&&&^6^3 110！)]： 1110 ；

^60 ；

731： 10331^(1

；

0"印"亿011^0

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1：1111 ；

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234

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^41^6？'：？^0011001801x1110^18/。卩8143111688 ^^1100110^18

^1(11 & 4 5 ^16^186

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、…：

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011效"0 3601163 ；

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；

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0^0

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711:162

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；

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，

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00 1 1 1 《 ' 肪 ' ^ 1 2

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Comparison of Different Models

7.1

Introduction

305

7.2

Models f o r Binary Targets: A n Example of Predicting Attrition

305

7.3

Models f o r Ordinal Targets: A n Example of Predicting Accident Risk ...316

7.4

Comparison o f All Three A c c i d e n t Risk Models

7.1

...327

Introduction This chapter compares the output and performance o f three modeling tools examined in the previous three chaptersâ&#x20AC;&#x201D;Decision Tree, Regression, and Neural N e t w o r k â&#x20AC;&#x201D; b y using all three tools to develop two types o f modelsâ&#x20AC;&#x201D;one with a binary target and one w i t h an ordinal target. I hope this chapter w i l l help you decide what approach to take. The model with a binary target is developed for a hypothetical bank that wants to predict the likelihood o f a customer's attrition so that it can take suitable action to prevent the attrition i f necessary. The details o f this model w i l l be presented later in this chapter. The model with an ordinal target is developed for a hypothetical insurance company that wants to predict the probability o f a given frequency o f insurance claims for each o f its existing customers and then use the resulting model to further profile customers who are most likely to have accidents.

7.2

Models for Binary Targets: A n Example of Predicting Attrition Attrition can be modeled as either a binary' or a continuous target. When you model attrition as a binary target, you predict the probability o f a customer "attriting" during the next few days or months, given the customer's general characteristics and change in the pattern o f the customers transactions. With a continuous target, you predict the expected time o f attrition, or the residual

306 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

lifetime, of a customer. For example, if a bank or credit card company wants to identify customers who are likely to terminate their accounts at any point within a pre-defined interval o f time in the future, the company can model attrition as a binary target. If, on the other hand, they are interested in predicting the specific time at which the customer is likely to attrit, then the company should model attrition as a continuous target, and use techniques such as survival analysis. In this chapter, I present an example of a hypothetical bank that wants to develop an early warning system to identify customers who are most likely to close their investment accounts in the next three months. To meet this business objective, an attrition model with a binary target is developed. The customer record in the modeling data set for developing an attrition model consists of three types of variables (fields): •

Variables indicating the customer's past transactions (such as deposits, withdrawals, purchase of equities, etc.) by month for several months.

•

Customer characteristics (demographic and socio-economic) such as age, income, lifestyle, etc.

•

Target variable indicating whether a customer attrited during a pre-specified interval or not.

Assume that the model was developed during December 2006, and it was used to predict attrition for the period January 1,2007, through March 31,2007. Display 7.1 shows a chronological view of a data record in the data set used for developing an attrition model. D i s p l a y 7.1 "

—

•

—

• "•

Customer transactions by month janOI ,2006 - June30, 2006.

Performance Window

Customer characteristics observed at time point on or before June 30,2006. Operational lag = 1 month

Event (Target variable) Attrition

Inputs / Explanatory variables window

No attrition = 0 ujg 1.2003 Jan 1.2006

June 30.2006

Oct 31

Chapter 7: Comparison of Different Models 307

Here are some key input design definitions that are labeled in Display 7.1: â&#x20AC;˘

Inputs/explanatory variables window This window refers to the period from which the transaction data is collected.

â&#x20AC;˘

Operational lag The model excludes any data for the month of July 2006 to allow for the operational lag that comes into play when the model is used for forecasting in real time. This lag may represent the period between the time at which a customer's transaction takes place and the time at which it is recorded in the database and becomes available to be used in the model.

â&#x20AC;˘

Performance window This is the time interval in which the customers in the sample data set are observed for attrition. I f a customer attrited during this interval then the target variable ATTR takes the value 1; i f not, it takes the value 0.

The model should exclude all data from any of the inputs that have been captured during the performance window. I f the model is developed using the data shown in Display 7.1, and used for predicting, or scoring, attrition propensity for the period January 1,2007, through March 2007, then the inputs window, the operation lag, and the prediction window will look like what is shown in Display 7.2 when the scoring or prediction is done. D i s p l a y 7.2

Customer transactions by month June 1,2006 - Nov 30.2D06

Prediction Window

Customer characteristics observed at tine point on or before June 30,2006. Operational lag = 1 month

Inputs /Explanatory variables window

Event (Target variable) Attntion

No attrition = 0 June 1.2006

Nov 30,2006

Jan 1,2007

March 31.2007

At the time of prediction (say on December 31, 2006), all data in the inputs/explanatory variables window would be available. In this hypothetical example, however, the inputs were not available for the month o f December 2006 because of the time lag in collecting the input data. In addition, no data will be available for the prediction window, because it resides in the future (see Display 7.2). As mentioned earlier, we assume that the model was developed during December 2006. The modeling data set was created by observing a sample of current customers with investment accounts during a time interval of three months starting August 1,2006, through October 31, 2006. This is the performance window, as shown in Display 7.1. The fictitious bank is interested in predicting the probability of attrition, specifically among their customers' investment accounts. Accordingly, i f an investment account is closed during the performance window, an indicator value o f 1 is entered on the customer's record; otherwise, a value o f 0 is given. Thus, for the target variable ATTR, 1 represents attrition and 0 represents no attrition.

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

Each record in the modeling data set includes the following details: •

monthly transactions

•

balances

•

monthly rates of change of customer balances

•

specially constructed trend variables.

These trend variables indicate patterns in customer transaction behavior, etc., for all accounts held by the customer during the inputs/ explanatory variables window, which spans the period of January 1, 2006, through June 30, 2006, as shown in Display 7.1. The customer's records also show how long the customer has held an investment account with the bank (investment account tenure). In addition, each record is appended with the customer's age, marital status, household income, home equity, life-stage indicators, and number of months the customer has been on the books (customer tenure), etc. All of these details are candidates for inclusion in the model. In the process flow shown in Display 7.3, the data source is created first. The Input Data node reads this data. The data is then partitioned such that 45% of the records are allocated to training, 35% to validation, and 25% for testing. I allocated more records for training and validation than for testing so that the models are estimated as accurately as possible. The value of the Partitioning Method property is set to Default, which performs stratification with respect to the target variable i f the target variable is categorical. I also imputed missing observations using the Impute node. Display 7.3 shows the process flow for modeling the binary target. D i s p l a y 7.3

Decision Tree (2)

7.2.1

Neural Network

Logistic Regression f o r Predicting A t t r i t i o n

The top segment of Display 7.3 shows the process flow for a logistic regression model o f attrition. In the Regression node, I set the Selection Model property to Stepwise, as this method was found to produce the most parsimonious model. Before making a choice of value for the Selection Criterion property, I tested the model using different values. In this example, the validation error criterion produced a model that made the most business sense. Display 7.4 shows the model that is estimated by the Regression node with these property settings.

Chapter 7: Comparison of Different Models

309

Display 7.4

Analysis

o r Maximum L i k e l i h o o d

Standard Parameter

Estimate

Error

Estimates

Maid Chi-Square

Pr >

ChiSq

Intercept

-1.3239

0.13S1

91.90

<.0001

btrend

-0.1505

0.00898

280.B9

<.0001

duration

-0.0294

0.00743

15.63

<.0001

The variables that are selected by the Regression node are B T R E N D and D U R A T I O N . The variable B T R E N D measures the trend in the customers balances during the six-month period prior to the operational lag period and the operational lag period. I f there is a downward trend in the balances over the period, e.g., a decline o f one percent, then the odds o f attrition w i l l increase by 1 0 0 * { e

o l s 0 5

- l } = 1 6 .24% . This may seem a bit high, but the direction o f the result does

make sense. (Also, keep in mind that these estimates are based on simulated data. It is best not to consider them as general results.) The variable D U R A T I O N measures the customers investment account tenure. Longer tenure corresponds to lower probability o f attrition. This can be interpreted as the positive effect o f customer loyalty. Display 7.5 shows the lift charts from the Results window o f the Regression node. D i s p l a y 7.5 rr Results - Rcgrcssta 1

D; dj Q tf_\

- nix

â&#x20AC;˘10

100

Decile j

TR^IM

VALIDATE |

Table 7.1 shows the cumulative lift and capture rates for the Train, Validate, and Test data sets.

310 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

T a b l e 7.1

Demidecile

Cumulative

Lift

Capture

Lift

3.44

17.2

3.03 2.82 2.48 2.26 2.18

30.3 42.3 49.5

65 70 75 80 85 SO 95 100

1.40 1.33 1.26 1.19 1.14 1.10 1.0S 1.00

2.03 1.90 1.78 1.67 1.57 1.46

56.6 65.3 71.1 75.8 80.0 83.3 86.2 87.8 90.8 93.2 94.4 95.3 96.9 98.8 99.3 100.0

Cumulative Lift

Cumulative Capture Rate(%)

3.49 2.93 2.85

17.4 29.3 42.7 53.9

Rate(%)

10 15 20 25 30 35 40 45 50 55 60

Cumulative Capture

3.63 3.03 2.60 2.41 2.21 2.10 1.96 1.84 1.78 1.66 1.56 1.47

18.1 30.3 39.1 48.1 55.3 63.1

1.39 1.33 1.25 1.20 1.14

68.4 73.4 80.0 82.8 85.9 88.4 90.6 92.8 93.4 95.6 97.2

1.09 1.05

98.4 99.7

1.00

100.0

2.69 2.47 2.31 2.15 2.01 1.86 1.70 1.57 1.47 1.39 1.33 1.27 1.21 1.15 1.10 1.05 1.00

i ;

i s

61.7 69.2 75.1 80.4 83.5 85.0 86.6 88.2 90.7 93.1 95.0 96.9 97.8 99.4 99.7

100.0

7.2.2 Decision Tree Model f o r Predicting A t t r i t i o n The middle section of Display 7.3 shows the process flow for the decision tree model of attrition. I set the Splitting Rule Criterion property to ProbChisq and the Subtree Method property to Average Square Error. My choice of these property values is somewhat arbitrary, but it resulted in a tree that can serve as an illustration. You can set alternative values for these properties and examine the trees produced. According to the property values I set, the nodes are split on the basis of /?-values of the Pearson Chi-Square, and the sub-tree selected is based on the average square error calculated from the validation data set. Display 7.6 shows the tree produced according to the above property values. The variables selected by the Decision Tree for splitting are BTREND and CV14. The variable BTREND is the trend in the customer balances, as explained earlier, and CV14 is a categorical variable, which reflects certain hypothetical economic clusters. The Regression node selected two variables, which were both numeric, while the Decision Tree selected one numeric variable and one nominal variable. One could either combine the results from both of these models or create a larger set of variables that would include the variables selected by the Regression node and the Decision Tree node.

In Tables 7.1 through 7.8,1 used the term demi-decile when I referred to the equal sized bins of the lift tables, since there are 20 of them. A more accurate term, perhaps, would be centiles, because the description found in the Demi-decile column is actually a S-percentile range indicator, e.g., 5, 10, IS... 90,95,100. In the Score Rankings Overlay window (shown in Displays 7.5, 7.7,7.8,7.9,7.10, etc.) and in the Store Rankings Overlay tables. Enterprise Miner refers to them as Deciles.

Chapter 7: Comparison of Different Models

311

Forecast combination is an important topic, and it deserves an in-depth study on its own. However, it is beyond the scope o f the present discussion. 2

Display 7.6

Display 7.7 shows the lift and cumulative lift o f the decision tree model.

Display 7.7 ra Itesulls - Decision Tree 1

File

EÂŤ

View

Wndow

Score Rankings Overlay: ATTR Cumulative Lift

For more information on forecast combination. See Brciman {1996a and 1996b), Sarma (2005), liklund and Karisson (2005), and Guidolin and Na (2007).

312 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

The cumulative lift and cumulative capture rates for Train, Validate, and Test data are shown in Table 7.2. T a b l e 7.2 â&#x20AC;˘

TREE Test

Validate

Train Deaidecile

Cumulative Lift

Cumulative Capture Rate(%)

Cumulative Lift

Cumulative Capture Rate(*)

Cumulative Lift

Cumulative Capture Rate(%)

5 10 15 20 25

3.37

16.9 32.0

3.06 3.05 2.58

15.3

3.00 3.00

15.0 30.0

2.34 2.19

30.5 38.7 46.8 54.9

66.2 72.2 75.5 78.8 82.0 85.3 88.6 90.6

2.10 1.95 1.81 1.69 1.61 1.53 1.47 1.39

62.9 68.3 72.3 76.3 80.3 84.3 88.3 90.6

91.9 93.3 94.6

1.31

91.9

1.31

82.4 85.3 88.2 90.3 91.7

1.24 1.18 1.13 1.08 1.04 1.00

93.3 94.6 96.0 97.3 98.7 100.0 .

1.24 1.18 1.13 1.08 1.04 1.00

93.1 94.5 95.9 97.2 98.6 100.0

30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

7.2.3

3.20 2.70 2.46 2.31 2.21 2.06 1.89 1.75 1.64 1.55 1.48 1.39 1.31 1.24 1.18 1.13 1.08 1.04 1.00

40.6 49.1 57.7

96.0 97.3 98.7 100.0

2.63 2.45 2.34 2.27

; !

39.S 49.0 58.5

68.1 73.9 76.7 79.6

2.11 1.92 1.77 1.65 1.55 1.47 1.39

| ; ;

i ; | | ]

| .... .

A Neural Network Model f o r Predicting A t t r i t i o n

The bottom segment of Display 7.3 shows the process flow for the neural network model o f attrition. In developing a neural network model of attrition, I tested the following two approaches: â&#x20AC;˘

Use all the inputs in the data set, set the Architecture property to M L P (multi/layer perceptron), the Selection Criterion property to Average Error, and the Number of Hidden Units property to 20.

â&#x20AC;˘

Use the same property settings as above, but use only selected inputs.

As expected, the first approach resulted in an over-fitted model. There was considerable deterioration in the lift calculated from the validation data set relative to that calculated from the training data set. The second approach yielded a much more robust model. With this approach, I tested different values for the Number of Hidden Units property. The models became more stable (as seen by comparing the lift charts from the training and validation data) as I increased the number of hidden units from the default value of 3 to 10 or 20.

Chapter 7; Comparison of Different Models

313

Using only a selected number o f inputs enables you to lest a variety o f architectural specifications fast. For purposes o f illustration. I present below the results from a model with the following properly settings: •

Architecture property: M L P

•

Selection Criterion property: 20

•

Number of Hidden Units property: 20

The bottom segment o f Display 7.3 shows the process How for the neural network model o f attrition. In this process flow I used the Decision Tree node with the Splitting Rule Criterion property set to ProbChisq and Assessment Measure property set to Average Square Error to select the inputs for use in the Neural Network node. The lift charts for this model are shown in Display 7.8. Display 7.8 1 to Results - Neural Netwoi k

Ffe

Ed-

View

Window

. J O l " ! M c u m U a u v e Lift

•3

£ 3 _l

imul

* >

52-

1 -1

60 Decile

TRAIN

100

VAUDATE |

Table 7.3 shows the cumulative lift and cumulative capture rates for the Train. Validate, and Test data sets.

314

Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business

Applications

Table 7.3 HEURAL Validate

Train j Dernidecile

5 10 IS 20 25 30 35 40 45 SO 55 60 65 70 75 60 85 90 95 100

Cumulative lift

3 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1

71 95 75 51 32 22 07 90 78 66 57 47 38 32 26 20 14 09 05 00

Cumulative Captute Rate %)

Cumulative Lift

Cumulative Capture Rate

5 5 3 2 0 7 5 76 1 SO 0 62 9 86 6 88 3 89 7 92 5 94 6 95 e 96 7 98 4 99 8

3.25 2.64 2.54 2.42 2.25 2.11 1.94 1.64 1.74

100 0

1.00

ie 29 41 50 58 66 72

Test

1.64 1.55 1.45 1.39 1.33 1.25 1.20 1.14 1.08 1.04

16 28 38 48 56 63 68 73

Cumulative Lift

%1 3 4 1 4 3 4 0 6

78 4 82 2 85 3 87 2 90 3 92 8 94 1 95 6 96 9 97 2 99 1 100 0

Cumulative Captute Rate [=0

3 3 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1

12 03 80 76 45 32 16 99 64 69 59 49 40 32 27 21 16 10 05

1 00

15.6 30.3 42.1 55.3 61.4 69.5 75.7 79.4 82.9 84.7 87.2 89.4 91.0 92.5 95.0 96.6 98.4 99.1 99.4 100.0

Display 7.9 shows the lift charts for all models from the Model Comparison node.

Display 7.9

hill

Display 7.10 shows the cumulative capture rates for the three models.

Chapter 7: Comparison of Different Models

315

Display 7.10

Table 7.4 shows a comparison o f the three models using test data.

Table 7.4 Regression Demidecile

Cumulative Life

5 10 15 20 30

3.49 2.93 2.85 2.69 2.47 2.31

35 40

2.15 2.01

45 50 55 60 65 70 75 30 65 90

1.86 1.70 1.57 1.47 1.39 1.33 1.27 1.21 1.15 1.10 1.05

95 100

1.00

Neural Network.

Cumulative Capture Rate(=0 17 29 42 S3 61

4 3 7

9 7 69 2 75 1 80 4 33 5 85 0 86 6 88 2 90 7 93 1 95 96 97 99 99 100

0 9 8 4 7 0

D e c i s i o n Tree

Cumulative Lift

Cumulative Capture Rate{%)

Cumulative Lift

Cumulative Capture Rate(*)

3.12 3.03 2.80 2.76 2.45

15.6 30.3 42.1 55.3 61.4 69.5

3.00 3.00 2.63 2.45 2.34 2.27

15 0 30 0 39 5 49 0 58 5 66 1

75.7 79.4 62.9 64.7 87.2 39.4 91.0 92.5 95.0 96.6 98.4 99.1 99.4 100.0

2.11 1.92 1.77

73 9 76 7 79 6 82 4 85 3 86 2 90 3 91 7

2.32 2.16 1.99 1.64 1.69 1.S9 1.49 1.40 1.32 1.27 1.21 1.16 1.10 1.05 1.00

1.65 1.55 1.47 1.39 1.31 1.24 1.18 1.13 1.08 1.04 1.00

93 1 94 S 95 9 97 2 98 6 100 0

From Table 7.4 it appears that both the logistic regression and neural network models have outperformed the decision tree model by a slight margin, and that the logistic regression model outperformed the neural network model by a slight margin. The top four deciles (the top eight demi-deciles). based on the logistic regression model, capture 80.4% o f attritors, while the top four deciles based on the neural network model capture 79.4% o f the attritors, while the top four

316

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

deciles, based on the decision tree model, capture 76.7% of attritors. The cumulative lift for all the three models are monotonically declining with each successive demi-decile.

7.3

Models for Ordinal Targets: An Example of Predicting Accident Risk In Chapter 5,1 demonstrated a neural network model to predict the loss frequency for a hypothetical insurance company. The loss frequency was a continuous variable, but I used a discrete version of it as the target variable. Here I will follow the same procedure, and create a discretized variable lossfrq from the continuous variable loss frequency. The discretization is done as follows: lossfrq takes the values 0, 1,2, and 3 according the following definitions: lossfrq = 0 if the loss frequency = 0 lossfrq = 1 if 0 < loss frequency < 1.5 lossfrq = 2 if 1.5 < loss frequency < 2.5 lossfrq = 3 if the loss frequency > 2.5. The goal here is to develop three models using the Regression, Decision Tree, and Neural Network nodes to predict the following probabilities: Pr(lossfrq = 0\X) = ?r(loss frequency = 0\X) ?v{lossfrq = 11^) = Pr(0 < loss frequency < 1.51X) ?r(lossfrq = 21X) = Pr(l .5 < loss frequency < 2.51X) ?v{lossfrq = 3 1 ^ ) = ?x{thelossfrequency> 2.51X) where X is a vector of inputs or explanatory variables. When you are using a target variable such as lossfrq, which is a discrete version of a continuous variable, the variable becomes ordinal i f it has more than two levels. The first model I develop for predicting the above probabilities is a proportional odds model (or logistic regression with cumulative logits link) using the Regression node. As explained in Section 6.2.2 of Chapter 6, the Regression node produces such a model i f the measurement level of the target is set to Ordinal. The second model I develop is a decision tree model using the Decision Tree node. The Decision Tree node does not produce any equations, but it does provide certain rules for portioning the data set into disjoint groups (leaf nodes). The rules are stated in terms o f input ranges (or definitions). For each group, the Decision Tree node gives the predicted probabilities: PrQossfrq = 01X),Pr{lossfrq = \\X\PrQossfrq = 21X) andPrQossfrq = ?>\X). The Decision Tree node also assigns a target level such as 0, 1,2, and 3 to each group, and hence to all the records belonging to that group. The third model will be developed by using the Neural Network node, which produces a Proportional Odds type model, provided I set the Target Activation Function property to Logistic, as explained in Section 5.5.1.1 of Chapter 5.

Chapter 7: Comparison of Different Models 317 Display 7.11 shows the process flow for comparing the ordinal target. D i s p l a y 7.11

Neural Network

7.3.1 Lift Charts and Capture Rates f o r Models w i t h Ordinal Targets Method Used b y S A S Enterprise Miner

When the target is ordinal, Enterprise Miner creates lift charts based on the probability of the highest level of the target variable. The highest level in this example is 3. For each record in the Test data set, Enterprise Miner computes the predicted, or posterior, probability Pr(lossfrq = 31X.) from the model. Then it sorts the data set in descending order of the predicted probability and divides the data set into 20 deciles, which I refer to alternatively as demi-deciles. Within each decile Enterprise Miner calculates the proportion of cases, as well as the total number o f cases, with lossfrq= 3. The lift for a decile is the ratio of the proportion of cases where lossfrq =3 in the decile to the proportion of cases with lossfrq=3 in the entire data set. The capture rate is the ratio of the total number of cases with lossfrq=3 within the decile to the total number of cases with lossfrq =3 in the entire data set. An Alternative Approach Using Expected Lossfrq

As pointed out earlier in Section 5.5.1.4, it is sometimes required to calculate lift and capture rate based on expected value of the target variable. For each model, I shall present lift tables and capture rates based on the expected value of the target variable. For each record in the Test data set, I calculate the expected lossfrq as E(lossfrq \X,) = Pr(lossfrq = 01X,,)*0 + Pr(lossfrq = 11X,)* l + Pr (lossfrq = 2\X,)*2 + Pr (lossfrq = 31 X,) * 3. Next, I sort the records of the data set in descending order of E(lossfrq \ X.), divide the data set into 20 demi-deciles (bins), and within each demi-decile, I calculate the mean of the actual or observed value of the target variable lossfrq. The ratio of the mean of actual lossfrq in a demidecile to the overall mean of actual lossfrq for the data set is the lift for the demi-decile. Likewise, the capture rate for a demi-decile is the ratio of the sum of the actual lossfrq for all the records within the demi-decile to the sum of the actual lossfrq of all the records in for the entire data set.

318

Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business

7.3.2

Applications

Logistic Regression with Proportional Odds f o r Predicting Risk in A u t o Insurance

Given that the measurement level o f the target variable is set to Ordinal, a proportional odds model is estimated by the Regression node using the property settings shown in Display 7.12. Display 7.12 Value

Property Node ID: imported Data Exported Data Variables [-Main Effects l-TwD-Fador Interactions [-PorynamialTerms [-Polynomial Degree i-[User Terms '-Term Editor

Yes vlO ••lo 2 No

[Regression Type '-Link Function

Logistic Regression Log it

[•Suppress Intercept • Input Coding -Mln Resource Use

No Deviation No

{•[Selection Model [-Selection Critenon '•Use Selection Default

Stepwise Validation Error NO

'. ^m^^OH^^^^^nWrlH^MPWHIrWI

E - ^ ! ;'v ''iiMiv. ::)•:•• [•Sequential Order ;

No 0.05 0.05

j-Entry Significance Level [ Stay Significance Level [•Start Variable Number [•Stop Variable Number [•Force Candidate Effects i-HierarchyEffects if" — - — • '-[•Moving Maximum Number Effect Rule of Steps

-•

[•iTechnlque

0 Class — None

Default

For the reasons I cited in the previous sections, I set the Selection Model property to Stepwise and the Selection Criterion property to Validation Error. The choice o f Validation E r r o r is also prompted by the lack o f an appropriate profit matrix. Display 7.13 shows the estimated equations for the cumulative logits. Display 7.13 A n a l y s i . of Haxiaua Likelihood E a t i n a t e s

Paraaeter Intercept Intercept Intercept ACE CRED HPKVID HPRVIG HPRVIO HPRVIO HPRVIO HPRVIO HPRVIO

DF 3

: l

0 i

2 3 4 5 6

1 1 1 1 1 1 1 1 1 1 1 1

Estimate -0.1100 1.8692 3.6020 -0.0344 -0.O0594 -1.9172 -1.4764 -0.7174

-1.2199 0.02S9 0.4649 2.4145

Standard Error

Uald Chi-Square

B.3948 0.3276 0.3219 0.00334 0.000407 O.1910 0.2050 0.2360 0.4144 0.4004 0.991B 0.4774

0.08 32, S2 125.18

105.69 213.2B 100.78 51.67 9.24

e.66 0.00 0.22 23.57

Pr > Ct)13q

Standardized Eatiaate

0.7S06

c.oooi <.0001 <.0001 <.O001

•r.oooi <-0001 0.0024 0.0032 0.9404 0.6392 <.0001

-0.2977 -0.30B3

E*p|Eat) 0.896 6.483 36.671 0.966 0.994 0.147 0.228 0.488 0.295 1.026 1.592 11.184

('hapter 7: Comparison of Different Models 319

It can be seen from Display 7.13 that the estimated model is logistic with a cumulative logits link. Accordingly, the slopes (coefficients o f the explanatory variables) for the three logit equations are the same, but the intercepts are different. There are only three equations in Display 7.13, while there are four events represented by the four levels (0, 1, 2. and 3) o f the target variable lossfrq. The three equations given in Display 7.13 plus the fourth equation, given by the identity

Prilossfrq = 0) + ?x(lossfrq = 1) + ?x{lossfrq = 2) + ?x{lossfrq = 3) - I are sufficient to solve for the probabilities o f the four events. (See Section 6.2.2 o f Chapter 6 to review how these probabilities are calculated by the Regression node.) In the model presented in Section 6.2.2, the target variable has only three levels, while the target variable in the model presented in Display 7.13 has four levels: 0, 1,2. and 3. The variables selected by the Regression node are A G E (age o f the insured), C R E D (credit score o f the insured), and N P R V I O (number o f prior violations). Because N P R V I O has only six levels. Enterprise Miner has treated it as a class input. The cumulative lift charts from the Results window o f the Regression node are shown in Display 7.14.

Display 7.14 1

•

. •

l l rS R e s u l t s F3e

View

Edit

M B 1:

. .

- Regression Window

#J DJjSJ

S c o r e R a n k i n g s O v e r l a y : LOSSFRQ ! ^ C u m u l a t i v e Lit!

Cumulative Lift

15-

0—1

Decile |

TRAIN

— V A L I D A T E|

The cumulative lift charts shown in Display 7.14 are based on the highest level o f the target variable, as described in Section 7.3.1. Using test data, alternative cumulative lift and capture rates based on E(lossfhf), as described in Section 7.3.1. were computed and are shown in Table 7.5.

320 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications T a b l e 7.5 ceg Loss Frequency DemiDecile

Number of Policies

Total

He en

Cumulative Total

Cumulative Mean

Cumulative Lift

Cumulative j Capture Rate(%)

450 451 451 450 4S1 451 451 450 451 451

S 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

126 57 60 39 43 24 19 22 12 14 13 13 7 7 6 10 10 9 5 0

451 450 451 451 451 450 451 4S1 451 451

100 —„

—

0.280 0.126 0.133 0.087 0.095 0.053 0.042 0.049 0.027 0.031 0.029 0.029 0.016 0.016 0.013 0.022 0.022 0.020 0.011 0.000

0.280 0.203 0.180 0.156 0.144

126 183 243 282 325 349 368

0.129 0.117 0.108

390 402 416 429 442 449 4S6 462 472 482 491 496

0.099 0.092 0.087 0.082 0.077 0.072 0.068 0.065 0.063 0.061 0.058 0.055

496

. . . . — __ „ . . . . . „ „ , . . „ ;

i 7 j r i

...

..,

5.09 3.69 3.27 2.84 2.62 2.35

25.4% 36.9% 49.0% 56.9%

\ j i t

65.5% 70.4%

f \

2.12 1.97

74.2% 78.6%

1.80 1.68 1.57 1.49

81.0% 83.9%

j ;

86.5% 89.1% 90.5% 91.9%

;• ; < |

93.1%

95.2% 97.2%

99.0%

1.39

1.31 1.24 1.19 1.14 1.10 1.05 1.00 . ...

100.0% .

100.0%

-.-}

7.3.3 Decision Tree Model f o r Predicting Risk i n A u t o Insurance Display 7.15 shows the settings of the properties of the Decision Tree node.

Chapter 7: Comparison of Different Models

321

Display 7.15 |

Properly

Node ID Imported Data Exported Data Variables Interactive

Value Tree

[Criterion [•Significance Level [•Missing Values [ Use Input Once [•Maximum Branch [-Maximum Depth "Minimum Categorical Size

ProbChisq 0-2 Jse in search No 2 6 5

[-Leaf Size [•Number of Rules [• Number of Surrogate Rules S p l i t Size

5 5 j

[•Exhaustive "Node Sample

5000 5000

[-Method [ Number of Leaves [•Assessment Measure ••-Assessment Fraction

Assessment

[- Bonferroni Adjustment [-Time of Kass Adjustment [• Inputs [ Number of Inputs "Split Adjustment

Yes Before No l Yes

;

Average Square Error 0.25

m| |l\W\ 111IIIWWWLWBMLMBMMMLW [ Variable Selection

A. •

[Yes

— i

Note that 1 set the Splitting Rule Criterion property to ProbChisq, the Subtree Method property to Assessment, and the Assessment Measure property to Average Square Error. These choices were arrived at after trying out different values. The settings chosen end up yielding a reasonably sized tree—one that is not too small or too large. In order tofind general rules for making these choices, you need to experiment with different values for these properties with many replications. Displays 7.16A, 7.16B, and 7.16C show the tree produced by the property settings given in Display 7.15.

322

Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business

D i s p l a y 7.16A

D i s p l a y 7.16B

Applications

Chapter 7: Comparison of Different Models

323

D i s p l a y 7.16C

CREO I

The Decision Tree node has selected the three variables A G E , CRED, and N P R V I O , which were also selected by the Regression node. In addition, the Decision Tree node selected t w o more variables N U M T R (number o f credit cards owned by the policyholder) and HEQ (value o f home equity). Display 7.17 shows the lift charts for the decision tree model. D i s p l a y 7.17 iff Retufte - Decision Tree Fte Edt View Whjow

O D| flj:fl ..JfiJ * J =e*e«Chart |cunJ»rveLift

ir,~

I"'a E

o 5 •

0~T

(}

[

60 Decile

TPAIIJ

—

VALIDATE!

100

324 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

Table 7.6 shows the lift charts calculated using the expected loss frequency. T a b l e 7.6 tcee Loss Frequency DealDecile

s 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Hunber of Policies

Total

450 451 451 450 451 451 451 450 451 451 451 450 451 451 451 450 451 451 451 451

108 48 78 25 29 36 13 25 15 15 17 9 9 8 8 12 18 S 3 15

Hean

0.240 0.106 0.173 0.056 0.064 0.080 0.029 0.056 0.033 0.033 0.038 0.020 0.020 0.018 0.018 0.027 0.040 0.011 0.007 0.033

Cuaulative Total

Cuaulative Hean

Cumulative Lift

Cunulative ]

108 156 234 259 288 324 337 362 377 392 409 418 427 435 443 455 473 478 481 496

0.240 0.173 0.173 0.144 0.128 0.120 0.107 0.100 0.093 0.087 0.082 0.077 0.073 0.069 0.066 0.063 0.062 0.059 0.056 0.055

4.36 3.15 3.15 2.61 2.32 2.18 1.94 1.83 1.69 1.58 1.50 1.40 1.32 1.25 1.19 1.15 1.12 1.07 1.02 1.00

21.8% 31.5% 47.2% 52.2% 58.1% 65.3% 67.9% 73.0% 76.0% 79.0%

Capture Rate<%)

82.5% 84.3% 86.1% 87.7% 89.3% 91.7% 95.4% 96.4% 97.0% 100.0%

j j

â&#x20AC;˘

7.3.4 Neural Network Model f o r Predicting Risk i n Auto Insurance The property settings for the Neural Network node are shown in Display 7.18.

Chapter 7: Comparison of Different Models

D i s p l a y 7.18

Property Node ID imported Data Exported Data Variables Use Current Estimates Architecture Direct Connection Model Selection Criterion Number of Hidden Units

j-Maximum Iterations Maximum Time Training Technique

Value ...

[Jo MLP NO Average Error 3 20 4 Hours Default

Normal Randomization Distribution 0.0 Randomization Center Randomization Scale 1.0 Standard Deviation inpulStandardization Yes Hidden Layer Hidden Layer Combination FunctioDefaull Hidden Layer Activation Function Default Yes Hidden Bias Target Layer Combination FunctiorDefault Logistic Target Layer Activation Function Default Target Layer Error Function Target Bias res BBBBBWI^IiUUI^I.,NO PreliminaryTralning 10 -Maximum Iterations I Hour [•Maximum Time -Number ofRuns 5 ©ConvergenceCriteria Yes -Default -1 34078E154 Absolute o -Absolute Function •Absolute Function Times i 1.0E-5 Absolute Gradient

•1

_ _

Because the number o f inputs available in the data set is small, 1 passed all o f them into the Neural Network node. I set the Number of Hidden Units property to its default value o f 3, since any increase in this value did not improve the results significantly. Display 7.19 shows the lift charts for the neural networks model.

325

326

Predictive

Modeling with SAS Enterprise

Miner: Practical Solutions for Business

Applications

D i s p l a y 7.19 rr* Results - Neural Network

Ffe

Eck View Window

Score Rankings Overlay: LOSSFRQ Cumulative Lit!

E O

100

Decile â&#x20AC;˘TRAIN

VALIDATE

Table 7.7 shows the cumulative lift and capture rates for the neural network model based on the expected loss frequency using the test data. T a b l e 7.7 \neural Loss

Demiecile

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 60 85 90 95 1Q0

[lumbet oE Policies

450 451 451 450 451 451 451 450 451 451 4S1 450 451 451 451 450 451 451 451 451

Frecruency

Total

Hean

11B if.:

0.262 0.140

45 38 35 33 19 19 19 20 19 10 7 15 10 7 7 6 4 2

0.100 0.064 0.078 0.073 0.042 0.042 0.042 0.044 0.042 0.022 0.01S 0.033 0.022 0.01S 0.016 0.013 0.009 0. 004

Cumulative Total

Cumulative Hean

Cumulative LiÂŁt

Cumulative Captur. Rate(%

118 181 226 264 299 332 351 370 389 409 428 438 445 460 478 477 434 490 494 496

0.262 0.201 0.167 0.147 0.133 0.123 0.111 0.103 0.096 0.091 0.086 0.081 0.076 0.073 0.070 0.066 0.063 0.060 0.058 0.055

4.77 3.65 3.04 2.66 2.41 2.23 2.02 1.87 1.74 1.65 1.57 1.47 1.38 1.32 1.26 1.20 1.15 1.10 1.05 1.00

23.8% 36.5% 45.6% 53. 2%

60.3k 66.9% 70.8% 74.6% 78.4% 82.5% 86. 3% 88.3% 89.7% 92.7% 94.8% 96.2% 97.6% 98.8% 99.6% 100,0%

Chapter 7: Comparison of Different Models 327

7.4 Comparison of All Three Accident Risk Models Table 7.8 shows the lift and capture rates calculated for the test data set ranked by E(lossjrq), outlined in Section 7.3.

T a b l e 7.8 Regcession DeniDecile

5 10 15 20 25 30 3S 40 45 50 S5 60 65 70 75 80 85 90 95 100

Cumulative Lift

5.09 3.69 3.27 2.84 2.62 2.35 2.12 1.97 1.80 1.68 1.57 1.49 1.39 1.31 1.24 1.19 1.14 1.10 1.05 1.00

Cumulative Capture Rate(%) 25.4% 36.9% 49.0% 56.9% 65.5% 70.4% 74.2% 78.6% 81.0% 83.9% 86.5% 89.1% 90.5% 91.9% 93.1% 95.2% 97.2% 99.0% 100.0% 100.0%

Neucal Hetvodc Cumulative LiCt

4.77 3.65 3.04 2.66 2.41 2.23 2.02 1.87 1.74 1.65 1.57 1.47 1.38 1.32 1.26 1.20 1.15 1.10 1.05 1.00

Cumulative Capture Rate(%) 23.8% 36.5% 45.6% 53.2% 60.3% 66.9% 70.8% 74.6% 78.4% 82.5% 86.3% 88.3% 69.7% 92.7% 94.8% 96.2% 97.6% 98.8% 99.6% 100.0%

Decision Tcee Cumulative LiCt

4.36 3.15 3.15 2.61 2.32 2.18 1.94 1.83 1.69 1.58 1.50 1.40 1.32 1.25 1.19 1.15 1.12 1.07 1.02 1.00

Cumulative Capture Rate(%) 21.8% 31.5% 47.2% S2.2% 58.1% 65.3% 67.9% 73.0% 76.0% 79.0% 82.5% 84.3% 66.1% 87.7% 89.3% 91.7% 95.4% 96.4% 97.0% 100.0%

From Table 7.8, it is clear that the logistic regression with a cumulative logits link is the winner in terms o f lift and capture rates. The table shows that, for the logistic regression, the cumulative lift at the eighth demi-decile (eighth row of the table) is 1.97. This means that the actual average loss frequency of the customers who are in the top eight demi-deciles (centiles 1 to 40) based on the logistic regression is 1.97 times the overall average loss frequency. The corresponding numbers for the neural networks and decision tree models are 1.87 and 1.83, respectively.

Customer Profitability

8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9

8.1

Introduction Acquisition Cost Cost of Default Revenue Profit.... The Optimum Cut-off Point.... Alternative Scenarios of Response and Risk Customer Lifetime Value Suggestions f o r Extending Results

329 331 333 334 334 336 337 338 ...........338

Introduction This chapter presents a general framework for calculating the profitability of different groups of customers using a simplified example. The methodology can be extended to calculate profits at the individual customer level as well. My goal here is to illustrate how costs of acquisition and the costs associated with risk factors can affect the decision to acquire new customers using the example of a credit card company. For example, suppose you have a population of 10,000 prospects. A response model is used to score each of these 10,000 prospects. The prospects are arranged in descending order of the predicted probability o f response and divided into deciles. A risk model is then used to calculate the risk rate for each prospect. In this simplified example, the risk rate is the probability o f a

330

Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business

Applications

customer defaulting on payment. Assume that a default results in a net loss o f $10 for the credit card company on the average. I f the customer did not default, the company would have made $100. (These numbers are fictitious, and 1 use them for demonstration purposes only.) Table 8.1 shows the estimated response and risk rates for the deciles o f the prospect population. T a b l e 8.1 Decile

Response Rate

Risk Rate

0 050

0 100

0.045

0 092

0.040

0 080

0.030

0 067

0020

0.059

0.010

0.042

0.005

0.038

0.004

0 029

0003

0.018

0002

0.010

From Table 8.1 you can see that response rate and risk rate move in the same direction. The response rate is highest in the first decile and declines with succeeding higher numbered deciles. A similar pattern is observed for the risk rate also. One reason why this might occur is that when a credit card company solicits applications for credit cards, the groups that respond most are likely to be those who cannot get credit elsewhere because o f their relatively high risk rates. In Table 8.1, the term risk rate is used in a general sense. A risk rate o f 10% in decile 1 in Table 8.1 means that 10% o f the persons who responded and are in decile I tend to default on their payments. (Here I am assuming that each person who responded is issued a credit card, but this assumption can be easily relaxed without violating the logic.) The response rate in a particular decile is the proportion o f individuals in the decile who are responders. Display 8.1 graphs the response and risk rates for each decile.

Chapter 8: Customer Profitability 331 D i s p l a y 8.1

PIOT2 PLOT

"***RlIk Rat.

O0O R . I J X V I . R a .

In Display 8.1, the horizontal axis shows the decile number. Decile 1 is the top decile, and decile 10 is the lowest decile. Note: The example presented in this chapter is hypothetical and greatly simplified for the purpose of exposition; the costs and revenue figures are also quite arbitrary. The example refers to a credit card company but it can be extended to insurance and other companies as well. Details of the methodology should be modified according to the specific situation being analyzed. In many situations, for example, customers with a high response rate to a direct mailing campaign might also pose higher risks to the company that is soliciting business.

8.2

Acquisition Cost New customers are acquired through channels such as newspaper or Internet advertisements, radio and TV broadcasting, direct mail, etc. I f a company spends X dollars on a particular channel, and as a result it acquires n customers, then the average cost of acquisition per customer \sX I n. For direct mail this can be calculated easily. Suppose the response rate is r in a segment of the target population and suppose the cost of sending one mail piece is m dollars. I f mail is sent to N customers from that segment, then the average cost of acquiring a customer

. N.m is

m = â&#x20AC;&#x201D; .

This means that, as the response rate decreases, the average acquisition cost increases. Display 8.1 A illustrates this relationship.

332

Predictive Modeling with SAS Enterprise

Miner: Practical Solutions for Business

Applications

D i s p l a y 8.1 A Avtd*3* AcqujidCTi Ceil

0 020

0 025

0A3O

Table 8.2 shows the acquisition cost by decile. Since the response rate declines with succeeding higher-numbered deciles, the average acquisition cost increases. When the company sends mail to the 1,000 prospects in the top decile (decile 1) inviting them to purchase a product, at a cost o f $1 per piece o f mail, the total cost is $1,000. In return, the company acquires 50 customers, as the response rate in the top decile is 0.05. The average cost o f acquisition is therefore $1,000/50 = $20. Similarly, the average cost o f acquisition for the second decile is $1,000/45 = $22.22. The last column shows the cumulative acquisition cost. I f all the prospects in the top two deciles are sent a mail piece, the total cost would be $2,000. This is the cumulative acquisition cost, and it is an important item in determining the optimum cut-off point for mailing. T a b l e 8.2 Decile

Customers

Cost per Mail-piece

Average Acquisition Cost

Total Acquisition Cost

Cumulative Acquisition Cost

$20

S 1.000

SI,000

S22

S1.000

52.000

Number

1000

S25

S1,000

53.000

1000

$33

S1.000

$4,000

1000

$50

51.000

S6.000

1000

5100

S1.000

56.000

1000

S200

SI.000

57.000

1000

S250

S1.000

$8,000

1000

S333

51,000

S9.000

1000

S500

51.000

S10.000

Chapter S: Customer Profitability

8.3

333

Cost of Default I f a customer defaults on payment, his credit card company incurs a loss. The credit card company might face other types o f risk, but for the purpose o f illustration only one type o f risk, the risk o f default, is considered here. Table 8.3 illustrates the costs due to the risk o f default. Assume, for the purpose o f illustration, that each event (default) results in a cost o f $10 to the credit card company. In general, you can include more events with associated probabilities and with more realistic costs in these calculations. In this simplified example, the top decile has 50 responders. O f these respondcrs. five people (the risk rate is 0.1) tend to default on their payments, resulting in an expected loss o f $50 to the company. In the second decile there are 45 responders (since the response rate is 4.5%). O f these 45 customers, 9.2% are likely to default on their payments resulting in an expected loss o f 45 0.092 x $10 = $ 4 1 . I f the company targets the top two deciles, it w i l l experience an expected loss o f $91. This is the cumulative loss for the second decile. Similarly cumulative losses can be calculated for the remaining deciles. x

T a b l e 8.3 Decile

Number of Responders

Risk Rate

Loss Loss per Amount Event

Cumulative Loss

0.100

$10

S50

0.092

S10

$41

S91

0080

S10

$32

$123

0.067

S10

$20

$144

0.059

$10

$12

$155

0.042

$10

$160

0.038

$10

$161

0.029

$10

S163

0018

$10

$163

0.010

$10

$163

The losses are calculated using the estimated probabilities, and hence, strictly speaking, they should be called expected losses. But here I will use the terms losses and expected tosses interchangeably.

334

8.4

Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business

Applications

Revenue Revenue depends on the number o f customers who do not default. These revenues are shown in Table 8.4. To simplify the example, I have assumed that a customer who defaults on payment generates no revenue. This assumption can easily be relaxed without violating the logic o f my argument. In the top decile, there are 50 responders, but only 45 o f them are customers in good standing at the end o f the year (assuming that our analysis is based on calculations for one year). Hence the revenue generated in the top decile is 45 x $100 = $4,500. A similar calculation yields revenue o f $4,086 for the second decile. Hence, the cumulative revenue for the first two deciles is $8,586. Similarly, cumulative revenue calculated for all the remaining deciles is shown in Table 8.4. T a b l e 8.4

Revenue

Cumulative Revenue

S100

54,500

0 092

S100

$4,086

$8,586

0 080

S100

$3,680

S12.266

0.067

S100

52,799

515,065

0 059

$100

31,882

S 16,947

0.042

S100

5958

S 17.905

5 0.038

S100

5481

S 18,386

0029

$100

$388

$18,774

3 0018

S100

S295

S 19,069

0.010

$100

5198

$19,267

Number of Responders

0.100

8.5

Risk Price Rate

Decile

Profit If, for example, the company targets the top two deciles, the expected revenue w i l l be $8,586, the expected acquisition cost w i l l be $2,000, and the expected losses w i l l be $91. Therefore, the expected cumulative profit w i l l be $8,586 - $2,000 - $ 9 1 = $6,495. Table 8.5 shows the cumulative profit for all the deciles.

Chapter 8: Customer Profitability

Table 8.5

335

_____ Decile

Cumulative Revenue

Cumulative Cost

Cumulative Profit

S4,500

S1.050

S3.450

$8,586

S2.091

S6.495

S12.266

$3,123

S9.143

515,065

$4,144

St 0.922

$16,947

$5,155

$11,792

SI 7,905

56,160

S11,746

S18,386

S7.161

511,225

S18.774

$8,163

$10,612

S 19.069

S9.163

S9.906

S19.267

S10.163

S9,104

The optimum cut-off point is where the cumulative profit peaks. In our example it occurs at decile 5. The company can maximize its profit by sending mail to only the 5.000 prospects who are in the top five deciles. This can be seen from Display 8.2.

The cumulative profit peaks at the fifth decile because, beyond the fifth decile, the marginal profit earned by mailing to an additional decile is negative.

336 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

The marginal cost at the fifth decile is defined as the additional cost that the company would incur in acquiring the responders in the next (sixth) decile. The marginal revenue at the fifth decile is defined as the additional revenue the company would earn i f it acquired all the responders in the next (sixth) decile. The marginal profit at the fifth decile is defined as the additional profit the company would make i f it acquired the responders in the next (sixth) decile, which is the marginal revenue minus the marginal cost. Beyond the fifth decile, the marginal cost outweighs the marginal revenue. Hence the marginal profit is negative. This is depicted in Display 8.3. D i s p l a y 8.3 Birr.

1 1

;-').!, i

•• • - — T T •• • ••

;

A,. .,-

nsrrwM/ynmr-f-i,

•

—i-mL.

H., .!.,,,^, 1

, mi

•.•, iii'iV, ,;i'w;ir-- H-'Vt„r^,.|. i

PtoJH ^ <000

8.6 The Optimum Cut-off Point The optimization problem can be analyzed in terms of marginal revenue and marginal cost. In this simplified example, marginal revenue (MR) at any decile is the additional revenue that is gained by mailing to the next decile, in addition to the previous deciles. Similarly, the marginal cost (MC) at any decile is the additional cost the company incurs by adding an additional decile for mailing. The marginal cost and marginal revenue, which are derived from the figures in Table 8.5, are shown in Table 8.6. From Table 8.5 it can be seen that, if the company mails to the top five deciles, the cumulative (total) revenue is $16,947 and cumulative (total) cost is $5,155. I f the company wants to mail to the top six deciles, the revenue will be $17,905 and cost will be $6,160. Hence the marginal revenue at 5 is $17,905 - $16,947 = $958 and marginal cost is $6,160 $5,155 = $1005. Since marginal cost exceeds marginal revenue, the cumulative profits will decline by adding the sixth decile to the mailing. The marginal revenues and marginal costs at different deciles shown in Table 8.6 are plotted in Display 8.4.

Chapter 8: Customer Profitability

337

T a b l e 8.6 Decile

Marginal Revenue

Marginal Cost

Profit by Decile

S4.500

S1.050

S3450

$4,086

0-11

53,045

53.660

$1,032

$2,648

$2,799

S1.020

SI,779

Si. 832

S1.012

S870

$958

S1004

$481

51,002

S-521

5388

SV001

$-613

$295

$1,001

S-706

S198

$1 000

S-302

D i s p l a y 8.4

â&#x20AC;˘

PLOT

- -.j-BCiB

From Display 8.4 it can be seen that the point at which M R = M C is somewhere between the fifth and sixth deciles. As an approximation, one can stop mailing after the fifth decile. In the above example the profits are calculated for one year only for the sake o f simplicity. Alternatively, you can calculate profits over a longer period o f time or add other complications to the analysis to make it more pertinent to the business problem that you are trying to solve. A l l these calculations can be made in the SAS Code node.

8.7

Alternative Scenarios of Response and Risk In the example presented in Table 8.1, it is assumed that response rate and risk rate move in the same direction. The response rate is highest in the first decile, and declines with succeeding higher numbered deciles. A similar pattern is observed for the risk rate. However, there arc many situations in which response rate and risk rate may not show this type o f a pattern. In such situations, you can estimate two scores for each customerâ&#x20AC;&#x201D;one based onprobability o f response.

338 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

and the other based on risk. Next, the prospects can be arranged in a 2x2 matrix, each cell of the matrix consisting of customers belonging to the same response-decile and the same risk-decile. Acquisition cost, cost o f risk, revenue, and profit can be calculated for each cell, and acquisition decisions can be made for each group of customers based on the profitability of the cells.

8.8 Customer Lifetime Value At the end o f Section 8.6 I pointed out that the analysis of customer acquisition based on the marginal profit from mailing to additional prospects could be performed with a more distant horizon than the one-year analysis shown in my example here. Taking this to its logical conclusion, calculations of profitability at the level of an individual customer can be extended further by calculating the customer's lifetime value. For this you need answers to three questions: â&#x20AC;˘

What is the expected residual lifetime of each customer?

â&#x20AC;˘

What is the flow of revenue that the customer is expected to generate in his "lifetime"?

â&#x20AC;˘

What are the future costs to the company of acquiring and retaining each customer?

Proper analysis of these elements can yield valuable insights into the type of actions that a company can take for extending the residual lifetime or customer lifetime value or both.

8.9 Suggestions for Extending Results In the analysis presented in this chapter, revenue, cost, and profit are calculated for each decile. Alternatively, you can do the same calculations for each percentile in your database o f prospects, rather than for each decile. In the above example, only one type of risk is identified. If, however, there is more than one type of risk, you can model competing risks by means o f logistic hazard functions using either the Regression node or the Neural Network node in Enterprise Miner.

Glossary

assessment

the process of determining how well a model computes good outputs from input data that is not used during training. Assessment statistics are automatically computed when you train a model with a modeling node. By default, assessment statistics are calculated from the validation data set.

association analysis rule

in association analyses, an association between two or more items. An association analysis rule should not be interpreted as a direct causation. An association analysis rule is expressed as follows: If item A is part of an event, then item B is also part of the event X percent of the time.

association discovery

the process of identifying items that occur together in a particular event or record. This technique is also known as market basket analysis. Association discovery rules are based on frequency counts of the number of times items occur alone and in combination in the database.

binary variable a variable that contains two discrete values (for example, PURCHASE: Yes and No),

branch

a subtree that is rooted in one of the initial divisions of a segment of a tree. For example, if a rule splits a segment into seven subsets, then seven branches grow from the segment.

CART (classification and regression trees)

a decision tree technique that is used for classifying or segmenting a data set. The technique provides a set of rules that can be applied to new data sets in order to predict which records will have a particular outcome. It also segments a data set by creating 2-way splits. The CART technique requires less data preparation than CHAID.

case

a collection of information about one of many entities that are represented in a data set. A case is an observation in the data set.

CHAID (chi-squared automatic interaction detection)

a technique for building decision trees. The CHAID technique specifies a significance level of a chi-square test to stop tree growth.

champion model

the best predictive model that is chosen from a pool of candidate models in a data mining environment. Candidate models are developed using various data mining heuristics and algorithm configurations. Competing models are compared and assessed using criteria such as training, validation, and test data fit and model score comparisons.

340 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

clustering

the process of dividing a data set into mutually exclusive groups such that the observations for each group are as close as possible to one another, and different groups are as far as possible from one another.

confidence

in association analyses, a measure of the strength of the association. In the rule A â&#x20AC;&#x201D;> B, confidence is the percentage of times that event B occurs after event A occurs. See also

association analysis rule. cost variable

a variable that is used to track cost in a data mining analysis.

data mining database (DMDB)

a SAS data set that is designed to optimize the performance of the modeling nodes. DMDBs enhance performance by reducing the number of passes that the analytical engine needs to make through the data. Each DMDB contains a meta catalog, which includes summary statistics for numeric variables and factor-level information for categorical variables.

data source

a data object that represents a SAS data set in the Java-based Enterprise Miner GUI. A data source contains all the metadata for a SAS data set that Enterprise Miner needs in order to use the data set in a data mining process flow diagram. The SAS data set metadata that is required to create an Enterprise Miner data source includes the name and location of the data set, the SAS code that is used to define its library path, and the variable roles, measurement levels, and associated attributes that are used in the data mining process.

data subdirectory

a subdirectory within the Enterprise Miner project location. The data subdirectory contains files that are created when you run process flow diagrams in an Enterprise Miner project.

decile

any of the nine points that divide the values of a variable into ten groups of equal frequency, or any of those groups.

dependent variable

a variable whose value is determined by the value of another variable or by the values of a set of variables.

depth

the number of successive hierarchical partitions of the data in a tree. The initial, undivided segment has a depth of 0.

diagram See process flow diagram.

Glossary 341

expected confidence

in association analyses, the number of consequent transactions divided by the total number of transactions. For example, suppose that 100 transactions of item B were made, and that the data set includes a total of 10,000 transactions. Given the rule A->B, the expected confidence for item B is 100 divided by 10,000, or one percent.

format

a pattern or set of instructions that SAS uses to determine how the values of a variable (or column) should be written or displayed. SAS provides a set of standard formats and also enables you to define your own formats.

generalization

the computation of accurate outputs, using input data that was not used during training,

hidden layer

in a neural network, a layer between input and output to which one or more activation functions are applied. Hidden layers are typically used to introduce nonlinearity.

hidden neuron

in a feed-forward, multilayer neural network, a neuron that is in one or more of the hidden layers that exist between the input and output neuron layers. The size of a neural network depends largely on the number of layers and on the number of hidden units per layer. See also

hidden layer. hold-out data

a portion of the historical data that is set aside during model development. Hold-out data can be used as test data to benchmark the fit and accuracy of the emerging predictive model. See also model.

imputation the computation of replacement values for missing input values,

input variable

a variable that is used in a data mining process to predict the value of one or more target variables.

internal node

in a tree, a segment that has been further segmented. See also node,

interval variable

a continuous variable that contains values across a range. For example, a continuous variable called Temperature could have values such as 0, 32,34, 36,43.5, 44, 56, 80, 99, 99.9, and 100.

Kohonen network

any of several types of competitive networks that were invented by Teuvo Kohonen. Kohonen vector quantization networks and self-organizing maps (SOMs) are two types of Kohonen network that are commonly used in data mining. See also Kohonen vector

quantization network, SOM (self-organizing map).

342 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

Kohonen vector quantization network

a type of competitive network that can be viewed either as an unsupervised density estimator or as an autoassociator. The Kohonen vector quantization algorithm is closely related to the k-means cluster analysis algorithm. See also Kohonen network.

leaf

in a tree diagram, any segment that is not further segmented. The final leaves in a tree are called terminal nodes.

level

a successive hierarchical partition of data in a tree. The first level represents the entire unpartitioned data set. The second level represents the first partition of the data into segments, and so on.

libref (library reference)

a name that is temporarily associated with a SAS library. The complete name of a SAS file consists of two words, separated by a period. The libref, which is the first word, indicates the library. The second word is the name of the specific SAS file. For example, in VLIB.NEWBDAY, the libref VLIB tells SAS which library contains the file NEWBDAY. You assign a libref with a LIBNAME statement or with an operating system command.

lift

in association analyses and sequence analyses, a calculation that is equal to the confidence factor divided by the expected confidence. See also confidence, expected confidence.

logistic regression

a form of regression analysis in which the target variable (response variable) represents a binary-level or ordinal-level response.

macro variable

a variable that is part of the SAS macro programming language. The value of a macro variable is a string that remains constant until you change it. Macro variables are sometimes referred to as symbolic variables.

measurement

the process of assigning numbers to an object in order to quantify, rank, or scale an attribute of the object.

measurement level

a classification that describes the type of data that a variable contains. The most common measurement levels for variables are nominal, ordinal, interval, log-interval, ratio, and absolute. See also interval variable, nominal variable, ordinal variable.

metadata a description or definition of data or information,

metadata sample

a sample of the input data source that is downloaded to the client and that is used throughout SAS Enterprise Miner to determine meta information about the data, such as number of variables, variable roles, variable status, variable level, variable type, and variable label.

model

a formula or algorithm that computes outputs from inputs. A data mining model includes information about the conditional distribution of the target variables, given the input variables.

Glossary 343

multilayer perceptron (MLP)

a neural network that has one or more hidden layers, each of which has a linear combination function and executes a nonlinear activation function on the input to that layer. See also

hidden layer. neural networks

a class of flexible nonlinear regression models, discriminant models, data reduction models, and nonlinear dynamic systems that often consist of a large number of neurons. These neurons are usually interconnected in complex ways and are often organized into layers. See also neuron.

neuron

a linear or nonlinear computing element in a neural network. Neurons accept one or more inputs. They apply functions to the inputs, and they can send the results to one or more other neurons. Neurons are also called nodes or units.

node

(1) in the SAS Enterprise Miner user interface, a graphical object that represents a data mining task in a process flow diagram. The statistical tools that perform the data mining tasks are called nodes when they are placed on a data mining process flow diagram. Each node performs a mathematical or graphical operation as a component of an analytical and predictive data model. (2) in a neural network, a linear or nonlinear computing element that accepts one or more inputs, computes a function of the inputs, and optionally directs the result to one or more other neurons. Nodes are also known as neurons or units. (3) a leaf in a tree diagram. The terms leaf, node, and segment are closely related and sometimes refer to the same part of a tree. See also process flow diagram, internal node.

nominal variable

a variable that contains discrete values that do not have a logical order. For example, a nominal variable called Vehicle could have values such as car, truck, bus, and train.

numeric variable

a variable that contains only numeric values and related symbols, such as decimal points, plus signs, and minus signs.

observation

a row in a SAS data set. All of the data values in an observation are associated with a single entity such as a customer or a state. Each observation contains either one data value or a missing-value indicator for each variable.

ordinal variable

a variable that contains discrete values that have a logical order. For example, a variable called Rank could have values such as 1,2, 3,4, and 5.

partition to divide available data into training, validation, and test data sets,

perceptron

a linear or nonlinear neural network with or without one or more hidden layers,

predicted value

in a regression model, the value of a dependent variable that is calculated by evaluating the estimated regression equation for a specified set of values of the explanatory variables.

344 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

process flow diagram

a graphical representation of the various data mining tasks that are performed by individual Enterprise Miner nodes during a data mining analysis. A process flow diagram consists of two or more individual nodes that are connected in the order in which the data miner wants the corresponding statistical operations to be performed.

profit matrix

a table of expected revenues and expected costs for each decision alternative for each level of a target variable.

project

a collection of Enterprise Miner process flow diagrams. See also process flow diagram,

root node

the initial segment of a tree. The root node represents the entire data set that is submitted to the tree, before any splits are made.

rule

See association analysis rule, sequence analysis rule, tree splitting rule, sampling

the process of subsetting a population into n cases. The reason for sampling is to decrease the time required for fitting a model.

SAS data set

a file whose contents are in one of the native SAS file formats. There are two types of SAS data sets: SAS data files and SAS data views. SAS data files contain data values in addition to descriptor information that is associated with the data. SAS data views contain only the descriptor information plus other information that is required for retrieving data values from other SAS data sets or from files whose contents are in other software vendors' file formats.

scoring

the process of applying a model to new data in order to compute outputs. Scoring is the last process that is performed in data mining.

seed

an initial value from which a random number function or CALL routine calculates a random value.

segmentation

the process of dividing a population into sub-populations of similar individuals. Segmentation can be done in a supervisory mode (using a target variable and various techniques, including decision trees) or without supervision (using clustering or a Kohonen network). See also

Kohonen network. self-organizing map See SOM (self-organizing map).

SEMMA the data mining process that is used by Enterprise Miner. SEMMA stands for Sample, Explore, Modify, Model, and Assess.

sequence analysis rule

in sequence discovery, an association between two or more items, taking a time element into account. For example, the sequence analysis rule A --> B implies that event B occurs after event A occurs.

Glossary 345

sequence variable

a variable whose value is a time stamp that is Used to determine the sequence in which two or more events occurred.

SOM (self-organizing map)

a competitive learning neural network that is used for clustering, visualization, and abstraction. A SOM classifies the parameter space into multiple clusters, while at the same time organizing the clusters into a map that is based on the relative distances between clusters. See also Kohonen network.

subdiagram

in a process flow diagram, a collection of nodes that are compressed into a single node. The use of subdiagrams can improve your control of the information flow in the diagram.

target variable

a variable whose values are known in one or more data sets that are available (in training data, for example) but whose values are unknown in one or more future data sets (in a score data set, for example). Data mining models use data from known variables to predict the values of target variables.

test data

currently available data that contains input values and target values that are not used during training, but which instead are used for generalization and to compare models.

training the process of computing good values for the weights in a model,

training data

currently available data that contains input values and target values that are used for model training.

transformation

the process of applying a function to a variable in order to adjust the variable's range, variability, or both.

tree

the complete set of rules that are used to split data into a hierarchy of successive segments. A tree consists of branches and leaves, in which each set of leaves represents an optimal segmentation of the branches above them according to a statistical measure.

tree splitting rule

in decision trees, a conditional mathematical statement that specifies how to split segments of a tree's data into subsegments.

validation data

data that is used to validate the suitability of a data model that was developed using training data. Both training data sets and validation data sets contain target variable values. Target variable values in the training data are used to train the model. Target variable values in the validation data set are used to compare the training model's predictions to the known target values, assessing the model's fit before using the model to score new data.

variable

a column in a SAS data set or in a SAS data view. The data values for each variable describe a single characteristic for all observations. Each SAS variable can have the following attributes: name, data type (character or numeric), length, format, informat, and label.

346 Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications

variable attribute

any of the following characteristics that are associated with a particular variable: name, label, format, informat, data type, and length.

variable level

the set of data dimensions for binary, interval, or class variables. Binary variables have two levels. A binary variable CREDIT could have levels of 1 and 0, Yes and No, or Accept and Reject. Interval variables have levels that correspond to the number of interval variable partitions. For example, an interval variable PURCHASE AGE might have levels of 0-18, 19-39,40-65, and >65. Class variables have levels that correspond to the class members. For example, a class variable HOMEHEAT might have four variable levels: Coal/Wood, FuelOil, Gas, and Electric. Data mining decision and profit matrixes are composed of variable levels.

References Afifi, A., V. Clark, and S. May. 2004. Computer Aided Multivariate Analysis. 4th ed. London: Chapman & Hall/CRC Press. Agresti, A. 2002. Categorical Data Analysis. 2d ed. New York: John Wiley & Sons. Allison, P. D. 2005. Fixed Effects Regression Methods for Longitudinal Data Using SAS. Cary, NC: SAS Institute Inc. Allison, P. D. 1999. Logistic Regression Using SAS: Theory and Application. Cary, NC: SAS Institute Inc. Bishop, C. M. 1995. Neural Networks for Pattern Recognition. New York: Oxford University Press. Breiman, L. 1996a. "Stacking Regressions." Machine Learning 24: 49-64. Breiman, L. 1996b. "Bagging Predictors." Machine Learning 24: 123-140. Cerrito, P. B. 2006. Introduction to Data Mining Using SAS Enterprise Miner. Cary, NC: SAS Institute Inc. Cody, R. P. 1999. Cody's Data Cleaning Techniques Using SAS Software. Cary, NC: SAS Institute Inc. deVille Barry. 2006. Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner. Cary, NC: SAS Institute Inc. Eklund, J., and S. Karlsson. 2005. "Forecast Combination and Model Averaging Using Predictive Measures." Working Paper Series 191, Sveriges Riksbank (Central Bank of Sweden). Friendly, M. 2000. Visualizing Categorical Data. Cary, NC: SAS Institute Inc. Freund, R. J., and R. C. Littell. 2000. SAS System for Regression. 3d ed. Cary, NC: SAS Institute Inc. Guidolin, M., and C. F. Na. 2007. "The Economic and Statistical Value of Forecast Combinations under Regime Switching: An Application to Predictable U.S. Returns," Working Paper Series 2006-059B, Federal Reserve Bank of St. Louis. Littell, R. C , W. W. Stroup, and R. J. Freund. 2002. SASfor Linear Models. 4th ed. Cary, NC: SAS Institute Inc. Maddala, G. S. 1986. "Limited-Dependent and Qualitative Variables in Econometrics." Econometric Society Monographs. New York: Cambridge University Press. Muller, K. E. and B. A. Fetterman. 2002. Regression and ANOVA: An Integrated Approach Using SAS Software. Cary, NC: SAS Institute Inc. Rawlings, J. O., S. G. Pantula, and D. A. Dickey. 2001. Applied Regression Analysis: A Research Tool. 2d ed. New York: Springer-Verlag. Ripley, B.D. 1996. Pattern Recognition and Neural Networks. New York: Cambridge University Press.

Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications Sarma, K. S. 2005. "Combining Decision Trees with Regression in Predictive Modeling with SAS Enterprise Miner." Proceedings of the Thirtieth Annual SAS Users Group International Conference. Cary, NC: SAS Institute. Sarma, K. S. 2001. "Using SAS Enterprise Miner for Forecasting." Proceedings of the Twentysixth Annual SAS Users Group International Conference. Cary, NC: SAS Institute. Sarma, K. S. 2001. "Using SAS Enterprise Miner for Forecasting Response and Risk." Proceedings of the Ninth Annual Conference of Western Users of SAS Software. Cary, NC: SAS Institute Inc. SAS Institute Inc. 2004. Advanced Predictive Modeling Using SAS Enterprise Miner 5.1 Course Notes. Cary, NC: SAS Institute Inc. SAS Institute Inc. 2007. Applied Analytics Using SAS Enterprise Miner 5 Course Notes. Cary, NC: SAS Institute Inc. SAS Institute Inc. 2007. Applying Data Mining Techniques Using SAS Enterprise Miner Course Notes. Cary, NC: SAS Institute Inc. SAS Institute Inc. 2005. Categorical Data Analysis Using Logistic Regression Course Notes. Cary, NC: SAS Institute Inc. SAS Institute Inc. 2004. Data Preparation for Data Mining Using SAS Software Course Notes. Cary, NC: SAS Institute Inc. SAS Institute Inc. 2001. Decision Tree Modeling Course Notes. Cary, NC: SAS Institute Inc. SAS Institute Inc. 2004. Extending SAS Enterprise Miner 5.1 Course Notes. Cary, NC: SAS Institute Inc. SAS Institute Inc. 2005. Neural Network Modeling Course Notes. Cary, NC: SAS Institute Inc. SAS Institute Inc. 2004. Predictive Modeling Using SAS Enterprise Miner 5.1 Course Notes. Cary, NC: SAS Institute Inc. SAS Institute Inc. 2004. SAS/STAT9.1 User's Guide. Cary, NC: SAS Institute Inc. Stokes, M. E., C. S. Davis, and G. G. Koch. 2000. Categorical Data Analysis Using the SAS System. 2d ed. Cary, NC: SAS Institute Inc. Svolba, G. 2006. Data Preparation for Analytics Using SAS. Cary, NC: SAS Institute Inc. Westfall, P. H., R. D. Tobias, D. Rom, R. D. Wolfinger, and Y. Hochberg. 1999. Multiple Comparisons and Multiple Tests Using SAS. Cary, NC: SAS Institute Inc.

Index

accident frequency See loss frequency acquisition cost 331-332 activation functions defined 172,175, 179-180 for predicting accident risk 316 for predicting loss frequency 204-207 for predicting response to mail campaign 181 hidden layer 172, 220-221, 223 specifying alternative architectures 219 target layer 172, 179-180, 204-207, 219, 223 ADD formula (combination function) 221 Advanced Metadata Advisor options 27, 110, 237 Akaike Information criterion, Selection Criteria property (Regression) 262,264-265 ANOVA (Analysis of Variance) 80,128 AOV16 variables accessing definitions 54, 57 Chi-Square values and 38 creating 50, 52 defined 50,79 interval-scaled inputs 79-85 AOV16 Variables property, Variable Selection node 82 arc tangent (arctan) function 175-176, 216 Architecture property, Neural Network node MLP setting 180, 216, 223, 312-313 User setting 220 Assess tab (Enterprise Data Miner) 22 Assessment Measure property, Decision Tree node Average Squared Error criterion 120,133, 135, 162,313, 321 Decision criterion 119-120, 133, 147 Lift criterion 120,133 Misclassification criterion 120, 133, 135, 141-142 options supported 119-120 attrition model 8-9,305-316 average profit, decision tree models 118, 120, 141 Average Squared Error criterion in decision tree models 120, 135, 142-143, 162,313, 321 in neural network models 185-186, 199

Backward elimination method 252-255, 262 Bernoulli error function 181, 207-208 bias observation weights and 11-12 width parameter and 217 binary targets binning transformations 60 in decision tree models 115, 122, 126 in neural network models 170, 179, 207 in regression models 236-237, 252-253, 255-256, 258-260 logistic regression and 27, 236 predicting attrition 8-9, 305-316 predicting response to mail campaign 2-4, 79, 90-99, 273-289 sampling method and 45 Variable Selection node and 49 binned variables See AOV16 variables Bonferroni Adjustment property 129-130 branches (decision trees) 115 business applications, Regression node 273-301

capture rates Cumulate % Captured Response chart 151-152, 189, 193 for models with ordinal targets 317 predicting accident risk 317,319, 326 predicting attrition 309, 312-314 predicting response to mail campaigns 275, 278-279, 283, 287 predicting savings increase 294-295, 298 categorial variables See also nominal variables See also ordinal targets comparing grouped and ungrouped 112 Cramer's V statistic 38, 72 defined 2 loss frequency as 204-213 measuring worth of split with 127 predicting targets 113 Variable Selection node 50, 79 CHAID tree 56,91

350 Index Chi-Square criterion, Variable Selection node binary targets 91-95 Bonferroni Adjustment property 129-130 calculating 73-75 Chi-Square Plot 37,74 description 166-167 measuring worth of splits 123-127 Transform Variables node 60-61 Variable Selection node 48-50,56-57,91-95 Chi-Square Plot 37,74 Chi-Square property, StatExplore node 38 child nodes 115 claim frequency See loss frequency Class Inputs property, Transform Variables node Dummy Indicators transformation 101 Group rare levels setting 279 None setting 105 setting default methods 62-63 Class Levels Count Threshold property 27-28, 110 Class Targets property, Transform Variables node 62 class variables defined 78 Filter node 46 grouped 85-89 Impute node 44 transformation methods 62 Variable Selection node 50, 52, 54 Cloglog option, Link Function property (Regression) 251 Cluster node 72 Code Location property, SAS Code node 69 combination functions defined 172, 181 for predicting loss frequency 204 for predicting response to mail campaign 181 hidden layer 172,220-223 radial basis function networks 223-231 target layer 172,204,220 continuous targets binary targets with 90-95 defined 2 measuring worth of split with 127-129 predicting attrition 305-306 predicting increased savings 273 predicting targets 113 regression models with 249, 253-255, 257-258, 260-261, 290-301 transformation methods 60-61, 99-100 with interval-scaled inputs 79-85 with nominal categorical inputs 85-89

cosine activation function 175 Cramer's V statistic 37-38,72-75 Create New Project window 20 Criterion property See Splitting Rule Criterion property, Decision Tree node Cross Validation Error criterion, Selection Criteria property (Regression) 262,268 Cross Validation Misclassification Rate criterion, Selection Criteria property (Regression) 262,269 Cross Validation Profit/Loss criterion, Selection Criteria property (Regression) 262, 272-273 Cumulate % Captured Response chart 151-152, 189, 193 Cumulative Lift charts assessing predictive performance 188 predicting accident risk 319, 326 predicting attrition 309-313 predicting response to direct campaign 149-150, 157, 184, 187, 277 predicting savings increase 295 cumulative logits 240-241 customer lifetime value 338 cut-off points in calculating profitability 336-337 ROC curves 194-195 D

data cleaning 12-14 data mining database (DMDB) 58-59 Data Options Dialog window 149-150 Data Partition node Data Set Percentage property 45 Filter node and 46 functionality 18 in process flow diagrams 44-45,99 in regression models 291 Partitioning Method property 45, 308 predicting response to mail campaign 146-147, 182,275 Test property 45,120,275 Training property 45, 120, 275 Validation property 45,120,275 Data Set Percentage property, Data Partition node 45 data sets applying decision tree models to 157-160 creating data sources for 22 dropping variables from 70-71 Neural Network node 170

Index 351 scoring 200-203,213-216 test 121 training 115,120-121 validation 121 viewing properties 33 Data Source property, Input Data node 35 Data Source Wizard 22-32 data sources 22-34, 110-112 DataSources folder 22 Decision criterion, Assessment Measure property (Decision Tree) 119-120, 133, 147 Decision Processing window 134 decision tree models See also Decision Tree node applying to prospect data 117-118 average profit 118, 120, 141 calculating worth 118-120 comparing different sizes 141 components 115-117 controlling growth 131 defined 91, 114-115 developing 121-129, 135-138 lift charts 323-324 logistic regression models vs. 117 predicting accident risk 320-324 predicting attrition with 310-312 predicting categorical targets 113 predicting response to direct marketing 144-160 pruning 121,132-135,138-140 selecting right-sized trees 141-143 size considerations 121 stopping rules 131 training data sets and 120-121 Decision Tree node See also Assessment Measure property, Decision Tree node calculating decision tree worth 119 changing options 22 functionality 15 in process flow diagrams 72 in regression models 274, 299 Leaf Role property 284 Leaf Size property 131, 284 Leaf Variable property 284 Maximum Depth property 131, 284 Method property 119, 133 predicting accident risk 161-162, 320-324 predicting attrition 310-312 predicting response to direct marketing 144-160, 283 Significance Level property 130-131, 284

Split Adjustment property 130-131 Split Size property 131 Splitting Criterion property 162,284 Splitting Rule Criterion property 123-127, 129, 310,313, 321 Subtree Method property 147, 284 training data 44 Transform Variables node and 61 Variable Selection node and 51, 80-81, 94, 166 Variable Selection property 284 decision weights See profit matrix Decision Weights tab (Data Source Wizard) 30, 118, 134 decisions calculating validation profits 139 defined 115-116,120 training data sets and 120, 137-138 Decisions property, Input Data node 118, 133, 145 Decisions tab (Data Source Wizard) 31 Default Filtering Method property, Filter node 46 Default Input Method property, Impute node 44, 275 defaulting on payments 333 Diagram Workspace (Enterprise Miner Window) Data Partition node in 45 description 22 Impute node in 44 SAS Code node in 66 Transform Variables node in 62 DMDB (data mining database) 58-59 DMINE procedure 58-59 Drop from Tables property, Drop node 70 Drop node Drop from Tables property 70 functionality 13, 18 in process flow diagrams 70-71 Variables property 71 Dummy Indicators transformation 62,99, 101 EHRADIAL formula (combination function) 222 Elliot function 175,177,216 Enterprise Miner 18-20 Enterprise Miner Window Assess tab 22 Diagram Workspace 22,44^5, 62, 66 Explore tab 22, 37 Help Panel 22 menu bar 21 Model tab 22 Modify tab 22,43

352 Index Enterprise Miner Window (continued) node group tabs 22 Project Panel 22-23,35 Properties Panel 22,45,47, 82, 94-95, 122 Sample tab 22 shortcut buttons 22 status bar 22 toolbar 22 Utility tab 22 Entropy criterion, Splitting Rule Criterion property (Decision Tree) 122-123,126-127 Entry Significance Level property, Regression node Forward selection method 255, 257 Stepwise selection method 258,260, 275 EQRADIAL formula (combination function) 222 EQSLOPES formula (combination function) 222 error function 180-181, 207-208 Error Sums of Squares 162 EVRADIAL formula (combination function) 222 EWRADIAL formula (combination function) 222 explanatory variables 172 Explore tab (Enterprise Data Miner) 22, 37 Exponential transformation 60 External File property, SAS Code node 69

F-test 123, 127-129 false positive fraction 193-195 Filter node Data Partition node and 46 Default Filtering Method property 46 functionality 14, 18 in process flow diagrams 45-48 Tables to Filter property 47 forward selection method 255-358 FREQ procedure 39,87

gauss activation function 175 generalized logits model 244 Gini Impurity index 122-123, 126-127 Group Rare Levels transformation 62,99 Group Variables property, Variable Selection node 86 grouped variables 85-89, 112

Help Panel (Enterprise Miner Window) 22 Hidden Layer Activation Functions property, Neural Network node 220-221,223

Hidden Layer Combination Function property, Neural Network node 220-223 hidden layers (neural network models) activation functions 172,220-221, 223 combination functions 172,220-223 defined 171-172 example of 172-178 specifying alternative architectures 219 hidden units (neural network model) 171,219 Hide property, Transform Variables node 65, 103, 105,279 histograms 40 hyperbolic tangent activation function 174-175 I identity link 251 impurity reduction 126-127 Impute node Default Input Method property 44,275 functionality 4,14, 18 in process flow diagrams 43-44, 99 predicting attrition 308 predicting response to mail campaign 275 Input Data node calculating decision tree worth 118 Data Source property 35 Decisions property 118, 133, 145 functionality 13,18 in process flow diagrams 35-36, 81,99 predicting attrition 308 predicting response to mail campaign 144-146, 158, 182 scoring data sets 213 Input Data Source node 275, 291 input layer (neural network model) 171, 173 input variables 14-15,41 inputs 172 inputs window 8 interest rates 6-8 intermediate calculations (neural network model) 171 intermediate nodes 91,115 intermediate outputs (neural network model) 172 Interval Inputs property, Transform Variables node binning transformations 60 Maximum Normal value 101, 103, 279-280 selecting default methods 62 interval-scaled targets See continuous targets Interval Targets property, Transform Variables node 62

Index 353 interval variables 2,44, 50 Interval Variables property, StatExplore node 38 Inverse transformation 60 Iteration Plot window 152-153, 185 L

leaf nodes (leaves) comparing different tree sizes 141 defined 91, 115, 160 in calculating worth of trees 117-120, 139 in developing trees 136, 155 in training data sets 136 validation data set and 121 Leaf Role property, Decision Tree node 284 Leaf Size property, Decision Tree node 131, 284 Leaf Variable property, Decision Tree node 284 lift charts capture rates for ordinal targets 317 Cumulative Lift chart 149-150,157 decision tree models 323-324 Model Comparison node 157, 314 models with ordinal targets 317 Neural Network node 170, 187, 210, 221, 312-313, 325 regression models 277, 282, 285-287, 309, 319 SAS Code node 65 Lift criterion, Assessment Measure property (Decision Tree) 120,133 Linear formula (combination function) 222 linear predictors 251 linear regression 250 Link Function property, Regression node Logit value 238-239, 250-251, 262 Probit value 251 log-odds (logits) 239-240 Log transformation 60 logistic function as sigmoid function 175,216 calculations 179-180 depicted 176 example usage 172 predicting loss frequency 204-207 predicting response to mail campaign 181 logistic regression binary targets and 27, 236 decision tree models vs. 117 logistic activation function and 181-182 predicting accident risk 318-320 predicting attrition 308-310 predicting response for mail campaign 275-289 Regression type property 249

Target Layer Activation Function property 204-205 with cumulative logits link 241 with generalized logits link 244 Logit option, Link Function property (Regression) 238-239, 250-251,262 logworth 124-126, 129-131 loss frequency calculating 4, 113, 127-129 in neural network models 170-171, 204-216 odds ratio and 239 predicting accident risk 160, 163-164, 204-216,317-319 M

macro variables 65, 67-69 macros 65, 67 maximal tree defined 121 in calculating validation profits 139 pruning 132 Maximum Correlation transformation 62 Maximum Depth property, Decision Tree node 131, 284 Maximum Normal transformation before variable selection 101, 103-105 overview 61-62 predicting increased savings 297 predicting response to mail campaign 279-280 measurement scales changing in data sources 110-112 for regression models 237 for Variable Selection node 78-99 for variables 2 menu bar (Enterprise Miner Window) 21 metadata 22,70-71 Metadata Advisor Option 26-27, 101 Method property, Decision Tree node 119, 133 Minimum Chi-Square property, Variable Selection node 50 Minimum R-Square property, Variable Selection node R-Square selection method 49, 51, 80, 83 transformation after variable selection 106 Misclassification criterion, Assessment Measure property (Decision Tree) categorical targets and 135 overview 120, 133 selecting right-sized trees 141-142 missing values 4, 14, 18 MLP (multilayer perceptron) 180, 216

354 Index Model Comparison node in process flow diagrams 72 lift charts 314 predicting response to mail campaign 275, 286, 288 regression tree example 162-163 ROC curves depicted 193-195 specifying alternative architectures 219 testing model performance 157, 187 Model Selection Criterion property, Neural Network node 181-182,208 Model tab (Enterprise Data Miner) 22 models/modeling 11-14,44 See also specific models Modify tab (Enterprise Data Miner) 22,43 multilayer perceptron (MLP) 180, 216 MultiPlot node functionality 18 in process flow diagrams 37,40-43 in regression models 262, 274 Type of Chart property 42 neural network models See also Neural Network node activation functions 172, 175, 179-180 alternative specifications 219-231 architecture of 171-172 combination functions 172,181 estimating weights 180-181 example of 172-180 predicting accident risk 324-326 predicting attrition 312-316 predicting response 181 -204 prediction loss frequency 204-216 radial basis functions 216-219, 223 scoring data sets 200-203, 213-216 target variables 170-171 Neural Network node Architecture property 180, 216, 220, 223, 312-313 changing options 22 data sets 170 Hidden Layer Activation Functions property 220-221,223 Hidden Layer Combination Function property 220-223 imputing missing values 14 in process flow diagrams 72 lift charts 170, 187, 210, 221, 312-313, 325 loss frequency 170-171

Model Selection Criterion property 181-182, 208 Number of Hidden Units property 312, 325 picking optimum weights 195-200 predicting accident risk 324-326 predicting attrition 312-316 predicting loss frequency 204-216 radial basis functions 216-219, 223-231 risk model 170 SAS Code node and 211 scoring data sets 200-203, 213-216 setting properties 182-187 specifying alternative architectures 219-231 specifying properties 171-172 Target Layer Activation Function property 204-207, 223,316 Target Layer Combination Function property 204, 220 Target Layer Error Function property 207-208 training data 44 neurons (units) 171 node group tabs (Enterprise Miner Window) 22 Node ID property, Transform Variables node 104 nodes defined 91,114-115, 160 training data set and 120, 138 nominal variables binary target with 95-99,237 continuous target with 85-89 defined 2 predicting targets 10-11 regression models with 244-249 normalized radial basis functions 219 NRBFEH function 223,227 NRBFEQ function 223,226 NRBFEV function 223,229 NRBFEW function 223, 225,228 NRBFUN function 223,230-231 Number of Bins property StatExplore node 38 Variable Selection node 50,91 Number of Hidden Units property, Neural Network Node 312,325

observation weights 11-12 odds ratio 239 operational lag 8 Optimal transformation 60-62, 103-105 optimal tree 121 optimum weights 180-182, 195-200 ORBFEQ function 223

Index 355 ORBFEW function 225 ORBFUN function 223-224 ordered polychotomous targets See ordinal targets ordinal targets defined 2 loss frequency as 204-213 predicting accident risk 316-326 regression models with 237-244 ordinary radial basis functions 219 original segment (decision trees) 114 outliers, eliminating 45 output layer (neural network model) 171-172, 179-180 over-sampling 12, 167-168

p- value adjustment options 129-130 controlling tree growth 131 measuring worth of splits 123-129 parent node 115 Partitioning Method property, Data Partition node 45, 308 Pearson's Chi-Square test 123-126, 166-167 performance window 8, 11 posterior probabilities association with leaf nodes 117 calculating validation profits 138-140 defined 115-116,160 developing 117 predicting loss frequencies 210 selecting right-sized trees 141 training data sets and 120, 136-138, 155 Principal Components node 72 prior probabilities 29-30,167,202 Prior Probabilities tab (Data Source Wizard) 29-30 ProbChisq criterion, Splitting Rule Criterion property (Decision Tree) Chi-Square method 126 partitioning data 122 predicting accident risk 321 predicting attrition 310, 313 predicting response to mail campaigns 284 ProbF criterion, Splitting Rule Criterion property (Decision Tree) 122, 129, 162 Probit option, Link Function property (Regression) 251 probnorm function 251 process flow diagrams creating 35-72 Data Partition node 44-^15, 99

Decision Tree node 72 diagram workspace for 22 Drop node 70-71 Filter node 4 5 ^ 8 Impute node 43-44, 99 Input Data node 35-36, 81, 99 Model Comparison node 72 Multiplot node 37,40-43 Neural Network node 72 Regression node 72, 81, 84 SAS Code node 65-70 StatExplore node 37-39 tools for exploring data 37-43 Transform Variables node 59-65, 99 Variable Selection node 48-59, 99 Profit/Loss criterion, Selection Criteria property (Regression) 262, 271-272 profit matrix (decision weights) calculating decision tree worth 118-119, 137 functionality 22, 30 Validation Profit/Loss criterion 269-271 profitability, calculating 329-338 Project Panel (Enterprise Miner Window) 22-23, 35 projects, creating 20 promotion window 6 properties See specific properties Properties Panel (Enterprise Miner Window) AOV16 Variables property 82 Chi-Square criterion 94-95 Data Partition node and 45 description 22 Filter node 47 Splitting Rule section 122 proportional odds model 171, 241 pruning decision trees 121,132-135, 138-140

R-Square criterion, Variable Selection node binary targets 90,96 continuous targets 79-85, 89 example 50-56 functionality 48-50 radial basis functions 216-219, 223-231 receiver operating characteristics (ROC) 193-195, 289 recursive partitioning 91,114,122 regression models See also logistic regression See also Regression node

356 Index regression models (continued) binary targets in 236-237, 252-253,255-256, 258-260 continuous targets in 249, 253-255, 257-258, 260-261,290-301 defined 121 developing 160-164 lift charts 277,282,285-287,309, 319 predicting accident risk 4-6, 113, 160-164, 318-320 predicting attrition with 308-310 Regression node See also Selection Criterion property, Regression node See also Selection Model property, Regression node business applications 273-301 Entry Significance Level property 255, 257-258, 260, 275 in process flow diagrams 72, 81, 84 Link Function property 238-239, 250-251, 262 models with binary targets 236-237,275-289 models with continuous targets 249,290-301 models with nominal targets 244-249 models with ordinal targets 237-244 predicting accident risk 318-320 predicting attrition 308-310 Regression Type property 238-239, 249-250, 262 SAS Code node and 69 Selection Default property 255, 258 Stay Significance Level property 252-254, 258, 260, 262, 275 training data 44 Transform Variables node and 103 Use Selection Default property 275, 291 Variable Selection node and 51,53, 80-81, 89-90, 93 Variables property 69, 84, 93 Regression Type property, Regression node 238-239, 249-250, 262 Reject property, Transform Variables node 65, 103, 105,279 response model Average Squared Error 120 lift measurement 120 misclassification rate 120 over-sampling and 12 predicting rate sensitivity to bank deposit products 6-8 predicting response to mail campaign 2-4, 113, 144-160, 181-204, 275-289

risk rate and 337-338 revenue, calculating 334 risk model calculating profitability 329-338 Neural Network node 170 predicting accident risk 4-6,113, 160-164, 204-216,316-326 response rate and 337-338 risk rate 329-330, 337-338 ROC (receiver operating characteristics) 193-195, 289 Role property 200 roles 31-32,42 root node defined 91, 114 in calculating validation profits 140 in training data sets 136-137 in validation data sets 121

sample comparability with target variables 11 partitioning in models 44 Sample tab (Enterprise Data Miner) 22 SAS Code node Code Location property 69 External File property 69 FREQ procedure 87 functionality 14, 18 in process flow diagrams 65-70 in regression models 296 lift charts 65 Neural Network node and 211 Regression node and 69 SAS Code property 66 Transform Variables node and 65, 103-104 SAS Code property, SAS Code node 66 Schwarz Bayesian criterion, Selection Criteria property (Regression) 262,265-266 score function 236 Score node 158,202, 213-216 Score Rankings Overlay window Cumulative Lift charts 149-150, 184, 187-188, 277 Cumulative % Captured Response chart 190 loss frequency example 163, 208-213 predicting savings increase 298 scoring the database 236 segments 114-115,160 Select a Chart Type window 41 Select Chart Roles window 41-42 Select Data Source window 35-36

Index 357 Selection Criterion property, Regression node Akaike Information criterion 262, 264-265 Average Error setting 312 Backward elimination method 252, 254 Cross Validation Error criterion 262, 268 Cross Validation Misclassification Rate criterion 262, 269 Cross Validation Profit/Loss criterion 262, 272-273 Forward selection method 255,257 overview 261-264 Profit/Loss criterion 262, 271-272 Schwarz Bayesian criterion 262, 265-266 Stepwise selection method 258, 260 Validation Error criterion 262, 266-267, 275, 283, 291-292, 297, 308 Validation Misclassification criterion 262, 267-268 Validation Profit/Loss criterion 262, 269-271 Selection Default property, Regression node 255, 258 Selection Model property, Regression node Backward elimination method 252-255, 262 Forward selection method 255-258 predicting attrition 308 Stepwise selection method 14-15, 258-261, 275, 284, 291,308 sensitivity, ROC curves reflecting 194 shortcut buttons (Enterprise Miner Window) 22 sigmoid functions 175, 216 Significance Level property, Decision Tree node 130-131,284 sine activation function 175 skewed variables 13-14 specificity, ROC curves reflecting 194-195 Split Adjustment property, Decision Tree node 130-131 Split Size property, Decision Tree node 131 Splitting Criterion property, Decision Tree node 162, 284 Splitting Rule Criterion property, Decision Tree node measuring worth of splits 123-127, 129 predicting accident risk 321 predicting attrition 310, 313 splitting values defined 60-61 measuring worth of splits 123-127 parti ti oni ng data 122-123 Square Root transformation 60 Square transformation 60

squared correlation coefficient See R-Square criterion, Variable Selection node Standardize transformation 60 StatExplore node Chi-Square Plot 74 Chi-Square property 38 functionality 13, 18 in process flow diagrams 37-39 in regression models 262, 274 Interval Variables property 38 Number of Bins property 38 status bar (Enterprise Miner Window) 22 Stay Significance Level property, Regression node Backward elimination method 252-254 predicting response to mail campaigns 275 Selection Criterion property examples 262 Stepwise selection method 258, 260 Stop R-Square property, Variable Selection node binary targets 90 continuous targets 80-81, 83-84 R-Square selection method 49,51 transformation after variable selection 106 stopping rules, decision trees 131 sub-segments 114 Sub-tree Assessment Measure property 165 Subtree Method property, Decision Tree node 147, 284 Sum of Squares 128 synthetic variables 179

Table Properties window (Data Source Wizard) 25-26 Tables to Filter property, Filter node 47 tanh function 216 target layer (neural network model) activation functions 172, 179-180, 204-207, 219 combination functions 172,204,220 defined 171-172 example of 179 Target Layer Activation Function property, Neural Network node 204-207,223,316 Target Layer Combination Function property, Neural Network node 204,220 Target Layer Error Function property, Neural Network node 207-208 target layers (neural network models) 223 Target Model property, Variable Selection node binary targets 96-97 continuous targets 81 R-Square selection method 49, 51, 56-57, 96-97

358 Index target variables comparability with sample 11 data cleaning 12-13 defining and measuring 2-11 examining relationships 41 for neural network models 170-171 predicting accident risk 4-6 predicting attrition 8-9 predicting nominal categorial targets 10-11 predicting rate sensitivity of bank deposit products 6-8 predicting response to mail campaign 2-4 transformations of 62 Targets tab (Data Source Wizard) 29 terminal nodes 91, 115, 136 test data sets predicting response to mail campaign 182 testing model performance 121,156-159 Test property, Data Partition node 45, 120, 275 Time of Kass Adjustment property 130 toolbar (Enterprise Miner Window) 22 training data sets controlling tree growth 131 Cross Validation Error criterion 268 Cross Validation Misclassification Rate criterion 269 developing decision trees 120-121, 135-138, 155 estimating weights in 180-181 node definitions in 115 predicting response to mail campaign 182 Profit/Loss criterion 271-272 selecting right-sized trees 141-142 Training property, Data Partition node 45, 120, 275 Transform Variables node after Variable Selection node 106-107 before Variable Selection node 101-105 Class Inputs property 62-63, 101, 105, 279 Class Targets property 62 Decision Tree node and 61 exporting code 108-109 functionality 18,77 Hide property 65, 103, 105, 279 in process flow diagrams 59-65, 99 in regression models 262, 274, 297 Interval Inputs property 60, 62, 103, 279-280 Interval Targets property 62 Node ID property 104 overriding default methods 63-64 Regression node and 103 Reject property 65, 103, 105, 279

SAS Code node and 65, 103-104 saving code 64-65,108-109 selecting default methods 62-63 types of transformations 99-100 Variables property 63, 65 Tree node 117 trees See decision tree models See regression models true positive fraction 193-195 Type of Chart property, MultiPlot node 42

units (neural network model) 171 unordered polychotomous variables See nominal variables Use AOV16 Variables property, Variable Selection node continuous targets 80, 85 R-Square selection method 50, 52, 57 transformation before variable selection 103 Use Group Variables property, Variable Selection node continuous targets 82, 85 R-Square selection method 50,52, 57 Use Selection Default property, Regression node 275,291 User Interface node 72 Utility tab (Enterprise Data Miner) 22 validation data sets decision trees and 121, 132-135, 142-143 finding optimum weights 181 predicting response to mail campaign 182 Validation Error criterion 266 Validation Misclassification criterion 266-268 Validation Profit/Loss criterion 269-271 Validation Error criterion, Selection Criteria property (Regression) overview 262,266-267 predicting attrition 308 predicting response to mail campaign 275, 283 predicting savings increase 291-292, 297 Validation Misclassification criterion, Selection Criteria property (Regression) 262, 267-268 Validation Profit/Loss criterion, Selection Criteria property (Regression) 262, 269-271 validation profits 121,138-140 Validation property, Data Partition node 45, 120, 275

Index 359 Variable Selection node See also R-Square criterion, Variable Selection node AOV16 Variables property 82 Chi-Square criterion 48-50,56-57 Decision Tree node and 51, 80-81, 94, 166 functionality 18,77 Group Variables property 86 in process flow diagrams 48-59, 99 measurement scale for 78-99 Minimum Chi-Square property 50 Minimum R-Square property 49,51, 80, 83,90, 96, 106 Number of Bins property 50,91 Regression node and 51, 53, 80-81, 89-90, 93 Stop R-Square property 49, 51, 80-81, 83-84, 90, 106 Target Model property 49, 51, 56-57, 81, 96-97 Transform Variables node after 106-107 Transform Variables node before 101 -105 transformation before 101 -105 Use AOV 16 Variables property 50,52, 57, 80, 85, 103 Use Group Variables property 50, 52, 57, 82, 85 with interval-scaled inputs 79-85, 90-95 with nominal categorical inputs 85-89, 95-99 Variable Selection property, Decision Tree node 284 variables See also specific types of variables assigning roles 42 changing measurement scales of 110-112 dropping 70-71 eliminating outliers 45 grouped 85 measurement scales for 2 skewed 13-14 synthetic 179 viewing 33-34 Variables property Drop node 71 for data sources 110 Regression node 69, 84, 93 Transform Variables node 63, 65 viewing list of variables 33

Weights-History window 197 whole tree 121

XRADIAL formula (combination function) 222

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