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

Combining generated filters


When building a predictive model, if many data fields are available to use as inputs to the model, then reducing the number of inputs can lead to better, simpler and easier-to-use models. Fields or features can be selected in a number of ways: by using business and data knowledge, by analysis to select individual fields that have a relation to the predictive target, and by using other models to select features whose relevance is more multivariate in nature.

In a Modeler stream, selections of fields are usually represented by Filter nodes. If multiple selections from the same set of fields have been produced, for example by generating Filter nodes from different models, then it is useful to combine these filters. Filters can be combined in different ways; for example, if we wish to select only the fields that were selected by both models, then the filters are placed in sequence. If we wish to select all the fields that were selected by either model, then a different...