sas enterprise miner操作手册英文版目录

Applied Analytics Using SAS® Enterprise Miner™
Course Notes
Applied Analytics Using SAS® Enterprise Miner™ Course Notes was developed by Peter Christie, Jim Georges, Jeff Thompson, and Chip Wells. Additional contributions were made by Tom Bohannon, Mike Hardin, Dan Kelly, Bob Lucas, and Sue Walsh. Editing and production support was provided by the Curriculum Development and Support Department.
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Applied Analytics Using SAS® Enterprise Miner™ Course Notes
Copyright © 2011 SAS Institute Inc. Cary, NC, USA. All rights reserved. Printed in the United States of America. 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.
Book code E2056, course code LWAAEM71/AAEM71, prepared date 18Oct2011. LWAAEM71_001
ISBN 978-1-61290-139-8
For Your Information iii Table of Contents
Course Description (x)
Prerequisites (xi)
Chapter 1 Introduction ......................................................................................... 1-1
1.1 Introduction to SAS Enterprise Miner ............................................................................. 1-3
1.2 Solutions ........................................................................................................................ 1-24
Solutions to Student Activities (Polls/Quizzes) ...................................................... 1-24 Chapter 2 Accessing and Assaying Prepared Data ........................................... 2-1
2.1 Introduction ...................................................................................................................... 2-3
2.2 Creating a SAS Enterprise Miner Project, Library, and Diagram ................................... 2-5
Demonstration: C reating a SAS Enterprise Miner Project ....................................... 2-6
Demonstration: C reating a SAS Library ................................................................. 2-10
Demonstration: C reating a SAS Enterprise Miner Diagram ................................... 2-12
Exercises ................................................................................................................. 2-13
2.3 Defining a Data Source .................................................................................................. 2-14
Demonstration: D efining a Data Source ................................................................. 2-18
Exercises ................................................................................................................. 2-33
2.4 Exploring a Data Source ................................................................................................ 2-34
Demonstration: E xploring Source Data .................................................................. 2-35
Demonstration: C hanging the Explore Window Sampling Defaults ...................... 2-59
Exercises ................................................................................................................. 2-61
Demonstration: M odifying and Correcting Source Data ........................................ 2-62
2.5 Chapter Summary .......................................................................................................... 2-74
2.6 Solutions ........................................................................................................................ 2-75
iv For Your Information
Solutions to Exercises ............................................................................................. 2-75
Solutions to Student Activities (Polls/Quizzes) ...................................................... 2-76 Chapter 3 Introduction to Predictive Modeling: Decision Trees ....................... 3-1 3.1 Introduction ...................................................................................................................... 3-3
Demonstration: C reating Training and Validation Data .......................................... 3-23 3.2 Cultivating Decision Trees ............................................................................................ 3-28
Demonstration: C onstructing a Decision Tree Predictive Model ........................... 3-43 3.3 Optimizing the Complexity of Decision Trees .............................................................. 3-61
Demonstration: A ssessing a Decision Tree ............................................................. 3-77 3.4 Understanding Additional Diagnostic Tools (Self-Study) ............................................. 3-88
Demonstration: U nderstanding Additional Plots and Tables (Optional) ................ 3-89 3.5 Autonomous Tree Growth Options (Self-Study) ........................................................... 3-94
Exercises ............................................................................................................... 3-102 3.6 Chapter Summary ........................................................................................................ 3-104 3.7 Solutions ...................................................................................................................... 3-105
Solutions to Exercises ........................................................................................... 3-105
Solutions to Student Activities (Polls/Quizzes) .................................................... 3-120 Chapter 4 Introduction to Predictive Modeling: Regressions ........................... 4-1 4.1 Introduction ...................................................................................................................... 4-3
Demonstration: M anaging Missing Values ............................................................. 4-18
Demonstration: R unning the Regression Node ....................................................... 4-24 4.2 Selecting Regression Inputs ........................................................................................... 4-28
Demonstration: S electing Inputs ............................................................................. 4-32 4.3 Optimizing Regression Complexity ............................................................................... 4-38
Demonstration: O ptimizing Complexity ................................................................. 4-40
For Your Information v 4.4 Interpreting Regression Models ..................................................................................... 4-49
Demonstration: I nterpreting a Regression Model ................................................... 4-51
4.5 Transforming Inputs ...................................................................................................... 4-52
Demonstration: T ransforming Inputs ...................................................................... 4-56
4.6 Categorical Inputs .......................................................................................................... 4-66
Demonstration: R ecoding Categorical Inputs ......................................................... 4-69
4.7 Polynomial Regressions (Self-Study) ............................................................................ 4-75
Demonstration: A dding Polynomial Regression Terms Selectively ....................... 4-77
Demonstration: A dding Polynomial Regression Terms Autonomously
(Self-Study) ................................................................................... 4-85 Exercises ................................................................................................................. 4-88
4.8 Chapter Summary .......................................................................................................... 4-89
4.9 Solutions ........................................................................................................................ 4-91
Solutions to Exercises ............................................................................................. 4-91
Solutions to Student Activities (Polls/Quizzes) .................................................... 4-104 Chapter 5 Introduction to Predictive Modeling: Neural Networks and
Other Modeling Tools ......................................................................... 5-1
5.1 Introduction ...................................................................................................................... 5-3
Demonstration: T raining a Neural Network ........................................................... 5-12
5.2 Input Selection ............................................................................................................... 5-20
Demonstration: S electing Neural Network Inputs .................................................. 5-21
5.3 Stopped Training ............................................................................................................ 5-24
Demonstration: I ncreasing Network Flexibility ..................................................... 5-35
Demonstration: U sing the AutoNeural Tool (Self-Study) ....................................... 5-39
5.4 Other Modeling Tools (Self-Study) ............................................................................... 5-46
Exercises ................................................................................................................. 5-58
5.5 Chapter Summary .......................................................................................................... 5-59
vi For Your Information
5.6 Solutions ........................................................................................................................ 5-60
Solutions to Exercises ............................................................................................. 5-60
Solutions to Student Activities (Polls/Quizzes) ...................................................... 5-63 Chapter 6 Model Assessment .............................................................................. 6-1 6.1 Model Fit Statistics .......................................................................................................... 6-3
Demonstration: C omparing Models with Summary Statistics .................................. 6-6 6.2 Statistical Graphics .......................................................................................................... 6-9
Demonstration: C omparing Models with ROC Charts ........................................... 6-13
Demonstration: C omparing Models with Score Rankings Plots ............................ 6-19
Demonstration: A djusting for Separate Sampling .................................................. 6-22 6.3 Adjusting for Separate Sampling ................................................................................... 6-29
Demonstration: A djusting for Separate Sampling (Continued) .............................. 6-32
Demonstration: C reating a Profit Matrix ................................................................ 6-35 6.4 Profit Matrices ............................................................................................................... 6-44
Demonstration: E valuating Model Profit ................................................................ 6-47
Demonstration: V iewing Additional Assessments .................................................. 6-49
Demonstration: O ptimizing with Profit (Self-Study) .............................................. 6-52
Exercises ................................................................................................................. 6-54 6.5 Chapter Summary .......................................................................................................... 6-55 6.6 Solutions ........................................................................................................................ 6-56
Solutions to Exercises ............................................................................................. 6-56
Solutions to Student Activities (Polls/Quizzes) ...................................................... 6-59 Chapter 7 Model Implementation ........................................................................ 7-1 7.1 Introduction ...................................................................................................................... 7-3 7.2 Internally Scored Data Sets ............................................................................................. 7-5
Demonstration: C reating a Score Data Source ......................................................... 7-6
For Your Information vii Demonstration: S coring with the Score Tool ............................................................ 7-7
Demonstration: E xporting a Scored Table (Self-Study) ......................................... 7-10
7.3 Score Code Modules ...................................................................................................... 7-16
Demonstration: C reating a SAS Score Code Module ............................................. 7-17
Demonstration: C reating Other Score Code Modules ............................................ 7-23
Exercises ................................................................................................................. 7-25
7.4 Chapter Summary .......................................................................................................... 7-26
7.5 Solutions to Exercises .................................................................................................... 7-27 Chapter 8 Introduction to Pattern Discovery ..................................................... 8-1
8.1 Introduction ...................................................................................................................... 8-3
8.2 Cluster Analysis ............................................................................................................... 8-8
Demonstration: S egmenting Census Data ............................................................... 8-16
Demonstration: E xploring and Filtering Analysis Data .......................................... 8-23
Demonstration: S etting Cluster Tool Options ......................................................... 8-34
Demonstration: C reating Clusters with the Cluster Tool ........................................ 8-38
Demonstration: S pecifying the Segment Count ...................................................... 8-41
Demonstration: E xploring Segments ...................................................................... 8-43
Demonstration: P rofiling Segments ........................................................................ 8-53
Exercises ................................................................................................................. 8-57
8.3 Market Basket Analysis (Self-Study)............................................................................. 8-59
Demonstration: M arket Basket Analysis ................................................................. 8-63
Demonstration: S equence Analysis ......................................................................... 8-79
Exercises ................................................................................................................. 8-82
8.4 Chapter Summary .......................................................................................................... 8-84
8.5 Solutions ........................................................................................................................ 8-85
Solutions to Exercises ............................................................................................. 8-85
Solutions to Student Activities (Polls/Quizzes) ...................................................... 8-96
viii For Your Information
Chapter 9 Special Topics ..................................................................................... 9-1 9.1 Introduction ...................................................................................................................... 9-3 9.2 Ensemble Models ............................................................................................................. 9-4
Demonstration: C reating Ensemble Models ............................................................. 9-5 9.3 Variable Selection ............................................................................................................ 9-9
Demonstration: U sing the Variable Selection Node ............................................... 9-10
Demonstration: U sing Partial Least Squares for Input Selection ........................... 9-14
Demonstration: U sing the Decision Tree Node for Input Selection ....................... 9-19 9.4 Categorical Input Consolidation .................................................................................... 9-23
Demonstration: C onsolidating Categorical Inputs .................................................. 9-24 9.5 Surrogate Models ........................................................................................................... 9-32
Demonstration: D escribing Decision Segments with Surrogate Models ................ 9-33 9.6 SAS Rapid Predictive Modeler ...................................................................................... 9-39
Demonstration: R unning a Basic RPM Model ....................................................... 9-41
Demonstration: R unning an Intermediate RPM Model (Self-Study)...................... 9-50
Demonstration: O pening an RPM Model in SAS Enterprise Miner ....................... 9-51
Demonstration: R egistering an RPM Model ........................................................... 9-53
Demonstration: S coring in SAS Enterprise Guide with a Registered Model ......... 9-54 Appendix A Case Studies ...................................................................................... A-1 A.1 Banking Segmentation Case Study ................................................................................. A-3 A.2 Web Site Usage Associations Case Study ..................................................................... A-19 A.3 Credit Risk Case Study ................................................................................................. A-22 A.4 Enrollment Management Case Study ............................................................................ A-40 Appendix B Bibliography ....................................................................................... B-1 B.1 References ....................................................................................................................... B-3
For Your Information ix Appendix C Index ................................................................................................... C-1
x For Your Information
Course Description
This course covers the skills required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models).
To learn more…
For information on other courses in the curriculum, contact the SAS Education
Division at 1-800-333-7660, or send e-mail to training@. You can also
find this information on the Web at /training/ as well as in the
Training Course Catalog.
For a list of other SAS books that relate to the topics covered in this
Course Notes, USA customers can contact our SAS Publishing Department at
1-800-727-3228 or send e-mail to sasbook@. Customers outside the
USA, please contact your local SAS office.
Also, see the Publications Catalog on the Web at /pubs for a
complete list of books and a convenient order form.
For Your Information xi Prerequisites
Before attending this course, you should be acquainted with Microsoft Windows and Windows-based software. In addition, you should have at least an introductory-level familiarity with basic statistics and regression modeling. Previous SAS software experience is helpful but not required.
xii For Your Information。