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

Using classification trees to explore the predictions of a Neural Network


Neural Nets have the reputation of being a black box technique; that is, that they are not highly revelatory of the reasoning behind their predictions. Compared to other techniques, information regarding what variables played the most important role in the model is fairly thin. It would be an exaggeration to say, however, that the Neural Net algorithm in Modeler provides no information; it does. Neural Nets are sometimes strong performers, and when they are the top performer they might be (and should be) a tempting option for Deployment. Is it possible to use other techniques to get a deeper insight into what the Neural Net has done behind the scenes? It is possible and one method for doing so is the subject of this recipe. We will be using CHAID to explore Neural Net predictions.

Getting ready

We will start with the Look Inside NN.str stream that uses the TELE CHURN MERGED data set.

How to do it...

To use classification...