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

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  • Formāts: 574 pages
  • Izdošanas datums: 20-Jul-2017
  • Izdevniecība: SAS Institute
  • Valoda: eng
  • ISBN-13: 9781635260403
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Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Third EditionPalielināt
  • Formāts: 574 pages
  • Izdošanas datums: 20-Jul-2017
  • Izdevniecība: SAS Institute
  • Valoda: eng
  • ISBN-13: 9781635260403
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A step-by-step guide to predictive modeling!Kattamuri Sarma's Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Third Edition, will show you how to develop and test predictive models quickly using SAS Enterprise Miner. Using realistic data, the book explains complex methods in a simple and practical way to readers from different backgrounds and industries. Incorporating the latest version of Enterprise Miner, this third edition also expands the section on time series. Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. Topics covered include logistic regression, regression, decision trees, neural networks, variable clustering, observation clustering, data imputation, binning, data exploration, variable selection, variable transformation, and much more, including analysis of textual data. Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis. Do it all with Predictive Modeling with SAS Enterprise Miner!
About This Book xi
About The Author xiii
Chapter 1 Research Strategy
1.1 Introduction
1(1)
1.2 Types of Inputs
2(1)
1.2.1 Measurement Scales for Variables
2(1)
1.2.2 Predictive Models with Textual Data
2(1)
1.3 Defining the Target
2(8)
1.3.1 Predicting Response to Direct Mail
2(2)
1.3.2 Predicting Risk in the Auto Insurance Industry
4(1)
1.3.3 Predicting Rate Sensitivity of Bank Deposit Products
5(2)
1.3.4 Predicting Customer Attrition
7(1)
1.3.5 Predicting a Nominal Categorical (Unordered Polychotomous) Target
8(2)
1.4 Sources of Modeling Data
10(1)
1.4.1 Comparability between the Sample and Target Universe
10(1)
1.4.2 Observation Weights
10(1)
1.5 Pre-Processing the Data
10(2)
1.5.1 Data Cleaning Before Launching SAS Enterprise Miner
11(1)
1.5.2 Data Cleaning After Launching SAS Enterprise Miner
11(1)
1.6 Alternative Modeling Strategies
12(1)
1.6.1 Regression with a Moderate Number of Input Variables
12(1)
1.6.2 Regression with a Large Number of Input Variables
13(1)
1.7 Notes
13(2)
Chapter 2 Getting Started with Predictive Modeling 15(124)
2.1 Introduction
16(1)
2.2 Opening SAS Enterprise Miner 14.1
16(1)
2.3 Creating a New Project in SAS Enterprise Miner 14.1
16(1)
2.4 The SAS Enterprise Miner Window
17(1)
2.5 Creating a SAS Data Source
18(9)
2.6 Creating a Process Flow Diagram
27(1)
2.7 Sample Nodes
27(29)
2.7.1 Input Data Node
27(2)
2.7.2 Data Partition Node
29(1)
2.7.3 Filter Node
29(4)
2.7.4 File Import Node
33(3)
2.7.5 Time Series Nodes
36(14)
2.7.6 Merge Node
50(3)
2.7.7 Append Node
53(3)
2.8 Tools for Initial Data Exploration
56(38)
2.8.1 Stat Explore Node
57(7)
2.8.2 MultiPlot Node
64(3)
2.8.3 Graph Explore Node
67(6)
2.8.4 Variable Clustering Node
73(9)
2.8.5 Cluster Node
82(3)
2.8.6 Variable Selection Node
85(9)
2.9 Tools for Data Modification
94(26)
2.9.1 Drop Node
94(1)
2.9.2 Replacement Node
95(3)
2.9.3 Impute Node
98(1)
2.9.4 Interactive Binning Node
99(7)
2.9.5 Principal Components Node
106(6)
2.9.6 Transform Variables Node
112(8)
2.10 Utility Nodes
120(6)
2.10.1 SAS Code Node
120(6)
2.11 Appendix to
Chapter 2
126(9)
2.11.1 The Type, the Measurement Scale, and the Number of Levels of a Variable
126(3)
2.11.2 Eigenvalues, Eigenvectors, and Principal Components
129(3)
2.11.3 Cramer's V
132(1)
2.11.4 Calculation of Chi-Square Statistic and Cramer's V for a Continuous Input
133(2)
2.12 Exercises
135(2)
Notes
137(2)
Chapter 3 Variable Selection and Transformation of Variables 139(56)
3.1 Introduction
139(1)
3.2 Variable Selection
140(22)
3.2.1 Continuous Target with Numeric Interval-scaled Inputs (Case 1)
140(7)
3.2.2 Continuous Target with Nominal-Categorical Inputs (Case 2)
147(6)
3.2.3 Binary Target with Numeric Interval-scaled Inputs (Case 3)
153(5)
3.2.4 Binary Target with Nominal-scaled Categorical Inputs (Case 4)
158(4)
3.3 Variable Selection Using the Variable Clustering Node
162(14)
3.3.1 Selection of the Best Variable from Each Cluster
164(10)
3.3.2 Selecting the Cluster Components
174(2)
3.4 Variable Selection Using the Decision Tree Node
176(3)
3.5 Transformation of Variables
179(11)
3.5.1 Transform Variables Node
179(2)
3.5.2 Transformation before Variable Selection
181(2)
3.5.3 Transformation after Variable Selection
183(2)
3.5.4 Passing More Than One Type of Transformation for Each Interval Input to the Next Node
185(4)
3.5.5 Saving and Exporting the Code Generated by the Transform Variables Node
189(1)
3.6 Summary
190(1)
3.7 Appendix to
Chapter 3
190(2)
3.7.1 Changing the Measurement Scale of a Variable in a Data Source
190(2)
3.7.2 SAS Code for Comparing Grouped Categorical Variables with the Ungrouped Variables
192(1)
Exercises
192(1)
Note
193(2)
Chapter 4 Building Decision Tree Models to Predict Response and Ris 195(84)
4.1 Introduction
196(1)
4.2 An Overview of the Tree Methodology in SAS® Enterprise Miner™
196(6)
4.2.1 Decision Trees
196(1)
4.2.2 Decision Tree Models
196(2)
4.2.3 Decision Tree Models vs. Logistic Regression Models
198(1)
4.2.4 Applying the Decision Tree Model to Prospect Data
198(1)
4.2.5 Calculation of the Worth of a Tree
199(2)
4.2.6 Roles of the Training and Validation Data in the Development of a Decision Tree
201(1)
4.2.7 Regression Tree
202(1)
4.3 Development of the Tree in SAS Enterprise Miner
202(19)
4.3.1 Growing an Initial Tree
202(7)
4.3.2 P-value Adjustment Options
209(2)
4.3.3 Controlling Tree Growth: Stopping Rules
211(1)
4.3.3.1 Controlling Tree Growth through the Split Size Property
211(1)
4.3.4 Pruning: Selecting the Right-Sized Tree Using Validation Data
211(2)
4.3.5 Step-by-Step Illustration of Growing and Pruning a Tree
213(5)
4.3.6 Average Profit vs. Total Profit for Comparing Trees of Different Sizes
218(1)
4.3.7 Accuracy/Misclassification Criterion in Selecting the Right-sized Tree (Classification of Records and Nodes by Maximizing Accuracy)
218(2)
4.3.8 Assessment of a Tree or Sub-tree Using Average Square Error
220(1)
4.3.9 Selection of the Right-sized Tree
220(1)
4.4 Decision Tree Model to Predict Response to Direct Marketing
221(15)
4.4.1 Testing Model Performance with a Test Data Set
230(1)
4.4.2 Applying the Decision Tree Model to Score a Data Set
231(5)
4.5 Developing a Regression Tree Model to Predict Risk
236(8)
4.5.1 Summary of the Regression Tree Model to Predict Risk
243(1)
4.6 Developing Decision Trees Interactively
244(25)
4.6.1 Interactively Modifying an Existing Decision Tree
244(22)
4.6.3 Developing the Maximal Tree in Interactive Mode
266(3)
4.7 Summary
269(1)
4.8 Appendix to
Chapter 4
270(5)
4.8.1 Pearson's Chi-Square Test
270(1)
4.8.2 Calculation of Impurity Reduction using Gini Index
271(1)
4.8.3 Calculation of Impurity Reduction/Information Gain using Entropy
272(2)
4.8.4 Adjusting the Predicted Probabilities for Over-sampling
274(1)
4.8.5 Expected Profits Using Unadjusted Probabilities
275(1)
4.8.6 Expected Profits Using Adjusted Probabilities
275(1)
4.9 Exercises
275(2)
Notes
277(2)
Chapter 5 Neural Network Models to Predict Response and Risk 279(90)
5.1 Introduction
280(1)
5.1.1 Target Variables for the Models
280(1)
5.1.2 Neural Network Node Details
281(1)
5.2 General Example of a Neural Network Model
281(9)
5.2.1 Input Layer
282(1)
5.2.2 Hidden Layers
283(5)
5.2.3 Output Layer or Target Layer
288(1)
5.2.4 Activation Function of the Output Layer
289(1)
5.3 Estimation of Weights in a Neural Network Model
290(1)
5.4 Neural Network Model to Predict Response
291(17)
5.4.1 Setting the Neural Network Node Properties
293(4)
5.4.2 Assessing the Predictive Performance of the Estimated Model
297(3)
5.4.3 Receiver Operating Characteristic (ROC) Charts
300(3)
5.4.4 How Did the Neural Network Node Pick the Optimum Weights for This Model?
303(2)
5.4.5 Scoring a Data Set Using the Neural Network Model
305(3)
5.4.6 Score Code
308(1)
5.5 Neural Network Model to Predict Loss Frequency in Auto Insurance
308(14)
5.5.1 Loss Frequency as an Ordinal Target
309(2)
5.5.1.1 Target Layer Combination and Activation Functions
311(10)
5.5.3 Classification of Risks for Rate Setting in Auto Insurance with Predicted Probabilities
321(1)
5.6 Alternative Specifications of the Neural Networks
322(8)
5.6.1 A Multilayer Perceptron (MLP) Neural Network
322(2)
5.6.2 Radial Basis Function (RBF) Neural Network
324(6)
5.7 Comparison of Alternative Built-in Architectures of the Neural Network Node
330(24)
5.7.1 Multilayer Perceptron (MLP) Network
332(1)
5.7.2 Ordinary Radial Basis Function with Equal Heights and Widths (ORBFEQ)
333(5)
5.7.3 Ordinary Radial Basis Function with Equal Heights and Unequal Widths (ORBFUN)
)335
5.7.4 Normalized Radial Basis Function with Equal Widths and Heights (NRBFEQ)
338(2)
5.7.5 Normalized Radial Basis Function with Equal Heights and Unequal Widths (NRBFEH)
340(3)
5.7.6 Normalized Radial Basis Function with Equal Widths and Unequal Heights (NRBFEW)
343(3)
5.7.7 Normalized Radial Basis Function with Equal Volumes (NRBFEV)
346(2)
5.7.8 Normalized Radial Basis Function with Unequal Widths and Heights (NRBFUN)
348(3)
5.7.9 User-Specified Architectures
351(3)
5.8 AutoNeural Node
354(2)
5.9 DMNeural Node
356(2)
5.10 Dmine Regression Node
358(2)
5.11 Comparing the Models Generated by DMNeural, AutoNeural, and Dmine Regression Nodes
360(2)
5.12 Summary
362(1)
5.13 Appendix to
Chapter 5
363(2)
5.14 Exercises
365(2)
Notes
367(2)
Chapter 6 Regression Models 369(84)
6.1 Introduction
369(1)
6.2 What Types of Models Can Be Developed Using the Regression Node?
369(14)
6.2.1 Models with a Binary Target
369(4)
6.2.2 Models with an Ordinal Target
373(6)
6.2.3 Models with a Nominal (Unordered) Target
379(4)
6.2.4 Models with Continuous Targets
383(1)
6.3 An Overview of Some Properties of the Regression Node
383(32)
6.3.1 Regression Type Property
384(1)
6.3.2 Link Function Property
384(2)
6.3.3 Selection Model Property
386(17)
6.3.4 Selection Criterion Property5
403(12)
6.4 Business Applications
415(27)
6.4.1 Logistic Regression for Predicting Response to a Mail Campaign
417(14)
6.4.2 Regression for a Continuous Target
431(11)
6.5 Summary
442(1)
6.6 Appendix to
Chapter 6
443(9)
6.6.1 SAS Code
443(4)
6.6.2 Examples of the selection criteria when the Model Selection property set to Forward.
447(4)
6.7 Exercises
451(1)
Notes
452(1)
Chapter 7 Comparison and Combination of Different Models 453(36)
7.1 Introduction
453(1)
7.2 Models for Binary Targets: An Example of Predicting Attrition
454(10)
7.2.1 Logistic Regression for Predicting Attrition
456(2)
7.2.2 Decision Tree Model for Predicting Attrition
458(2)
7.2.3 A Neural Network Model for Predicting Attrition
460(4)
7.3 Models for Ordinal Targets: An Example of Predicting the Risk of Accident Risk
464(12)
7.3.1 Lift Charts and Capture Rates for Models with Ordinal Targets
465(1)
7.3.2 Logistic Regression with Proportional Odds for Predicting Risk in Auto Insurance
466(3)
7.3.3 Decision Tree Model for Predicting Risk in Auto Insurance
469(4)
7.3.4 Neural Network Model for Predicting Risk in Auto Insurance
473(3)
7.4 Comparison of All Three Accident Risk Models
476(1)
7.5 Boosting and Combining Predictive Models
476(10)
7.5.1 Gradient Boosting
477(2)
7.5.2 Stochastic Gradient Boosting
479(1)
7.5.3 An Illustration of Boosting Using the Gradient Boosting Node
479(3)
7.5.4 The Ensemble Node
482(3)
7.5.5 Comparing the Gradient Boosting and Ensemble Methods of Combining Models
485(1)
7.6 Appendix to
Chapter 7
486(2)
7.6.1 Least Squares Loss
486(1)
7.6.2 Least Absolute Deviation Loss
486(1)
7.6.3 Huber-M Loss
487(1)
7.6.4 Logit Loss
487(1)
7.7 Exercises
488(1)
Note
488(1)
Chapter 8 Customer Profitability 489(10)
8.1 Introduction
489(2)
8.2 Acquisition Cost
491(1)
8.3 Cost of Default
492(1)
8.5 Profit
493(2)
8.6 The Optimum Cutoff Point
495(1)
8.7 Alternative Scenarios of Response and Risk
496(1)
8.8 Customer Lifetime Value
496(1)
8.9 Suggestions for Extending Results
497(1)
Note
497(2)
Chapter 9 Introduction to Predictive Modeling with Textual Data 499(48)
9.1 Introduction
499(8)
9.1.1 Quantifying Textual Data: A Simplified Example
500(3)
9.1.2 Dimension Reduction and Latent Semantic Indexing
503(3)
9.1.3 Summary of the Steps in Quantifying Textual Information
506(1)
9.2 Retrieving Documents from the World Wide Web
507(2)
9.2.1 The %TMFILTER Macro
507(2)
9.3 Creating a SAS Data Set from Text Files
509(3)
9.4 The Text Import Node
512(2)
9.5 Creating a Data Source for Text Mining
514(2)
9.6 Text Parsing Node
516(5)
9.7 Text Filter Node
521(7)
9.7.1 Frequency Weighting
521(1)
9.7.2 Term Weighting
521(1)
9.7.3 Adjusted Frequencies
521(1)
9.7.4 Frequency Weighting Methods
521(2)
9.7.5 Term Weighting Methods
523(5)
9.8 Text Topic Node
528(6)
9.8.1 Developing a Predictive Equation Using the Output Data Set Created by the Text Topic Node
533(1)
9.9 Text Cluster Node
534(12)
9.9.1 Hierarchical Clustering
535(1)
9.9.2 Expectation-Maximization (EM) Clustering
536(6)
9.9.3 Using the Text Cluster Node
542(4)
9.10 Exercises
546(1)
Notes
546(1)
Index 547
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