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 (11 chapters)
10
Index

Introduction


This set of recipes contains tricks and shortcuts for tasks that most analysts would anticipate as central to data preparation. Two subtasks are addressed, integrate and format. The first four recipes involve aspects of integration and the last two involve aspects of format.

The first recipe makes use of the optimization settings in the Merge node. By combining this feature with some preparation steps in the stream, one can handle data sets of considerable size. The next recipe takes as its starting point the flexibility that can come from using the core features of Modeler that date back to the earliest versions. Many recently added nodes automate routine tasks, such as SetToFlag. However, many of these same tasks were possible in Modeler in earlier versions. With convenience sometimes come limitations. Shuffle Down uses this approach to produce a nonstandard aggregation. The next two recipes address typical merge conditions. Finally, the issue of formatting is addressed with...