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

Creating a classification tree financial summary using aggregate and an Excel Export node


In nearly all organizations, the Modeler user collaborates with colleagues that do not have Modeler. If one is to honor the Value Law quoted in the chapter introduction, criteria must be considered other than accuracy and stability. Since ROI is on the mind of the data miner, those additional criteria always include some of a financial nature. Whether it be potential cost savings or potential revenue increases, the relevant fields will be ones that management will be very familiar with and very interested in. This is not to say that these variables must be model inputs—quite often they are not—but they should be part of the evaluation process. In this recipe, we will process an Excel file that organizes these kinds of variables in the context of a tree segmentation. It combines variables exported from the tree model, including Rule Identifier and the Model's prediction, with financial variables in summary...