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

Selecting variables using the Means node


In this recipe we will identify and select variables to include as model inputs using the Means node.

Getting ready

This recipe uses the datafile cup98lrn_reduced_vars3.sav and the stream recipe_variableselection_means.str.

You will need a copy of Microsoft Excel to visualize the list of rules (optional).

How to do it...

To identify and select variables to include as model inputs using the Means node:

  1. Open the stream variableselection_means.str by navigating File | Open Stream.

  2. Make sure the datafile points to the correct path to the file cup98lrn_reduced_vars3.sav.

  3. Open the Means node to look at the options. Note that the grouping variable is our target variable TARGET_B, and the test fields are all the continuous variables of interest as shown in the following figure.

  4. Run the Means node by clicking on Run.

  5. Inside the output window, click on the Importance column twice so that the variables are sorted in descending order of Importance as shown in the following...