Book Image

IBM SPSS Modeler Cookbook

Book Image

IBM SPSS Modeler Cookbook

Overview of this book

IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide.
Table of Contents (17 chapters)
IBM SPSS Modeler Cookbook
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Merging a lookup table


Nominal variables with more than several categories pose a potential problem. First, fields with a large number of categories can significantly increase processing time. Second, these fields can potentially have categories with very few cases, which can become problematic (for example, they might be outliers or just difficult to understand). Third, these fields might not even be used by certain models (see the following screenshot). Finally, fields with a large number of categories might not really get at the crux of the real characteristics of interest. Many new users of Modeler don't realize that many algorithms are automatically transforming nominal variables behind the scenes. Within the General Setting in Stream Properties, there are two options designed to prevent this problem from getting out of hand.

As mentioned earlier, many times fields with a large number of categories might not really get at the real characteristics of interest and therefore sometimes it...