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Copyright © 2010, SAS Institute Inc. All rights reserved.
Applied Analytics Using SAS® Enterprise Miner™
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Chapter 1 Introduction1.1 Introduction to SAS Enterprise Miner
1.2 Solutions
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Chapter 2 Accessing and Assaying Prepared Data2.1 Introduction
2.2 Creating a SAS Enterprise Miner Project, Library, and Diagram
2.3 Defining a Data Source
2.4 Exploring a Data Source
2.5 Chapter Summary
2.6 Solutions
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Chapter 3 Introduction to Predictive Modeling: Decision Trees3.1 Introduction
3.2 Cultivating Decision Trees
3.3 Optimizing the Complexity of Decision Trees
3.4 Understanding Additional Diagnostic Tools (Self-Study)
3.5 Autonomous Tree Growth Options (Self-Study)
3.6 Chapter Summary
3.7 Solutions
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Chapter 4 Introduction to Predictive Modeling: Regressions4.1 Introduction
4.2 Selecting Regression Inputs
4.3 Optimizing Regression Complexity
4.4 Interpreting Regression Models
4.5 Transforming Inputs
4.6 Categorical Inputs
4.7 Polynomial Regressions (Self-Study)
4.8 Chapter Summary
4.9 Solutions
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Chapter 5 Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools5.1 Introduction
5.2 Input Selection
5.3 Stopped Training
5.4 Other Modeling Tools (Self-Study)
5.5 Chapter Summary
5.6 Solutions
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Chapter 6 Model Assessment6.1 Model Fit Statistics
6.2 Statistical Graphics
6.3 Adjusting for Separate Sampling
6.4 Profit Matrices
6.5 Chapter Summary
6.6 Solutions
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Chapter 7 Model Implementation7.1 Introduction
7.2 Internally Scored Data Sets
7.3 Score Code Modules
7.4 Chapter Summary
7.5 Solutions to Exercises
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Chapter 8 Introduction to Pattern Discovery8.1 Introduction
8.2 Cluster Analysis
8.3 Market Basket Analysis (Self-Study)
8.4 Chapter Summary
8.5 Solutions
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Chapter 9 Special Topics9.1 Introduction
9.2 Ensemble Models
9.3 Variable Selection
9.4 Categorical Input Consolidation
9.5 Surrogate Models
9.6 SAS Rapid Predictive Modeler
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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. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright © 2011 SAS Institute Inc. Cary, NC, USA. All rights reserved. Prepared 18OCT2011.
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