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

Removing redundant variables using correlation matrices


In this recipe we will remove redundant variables by building a correlation matrix that identifies highly correlated variables.

Getting ready

This recipe uses the datafile, nasadata.txt and the stream file, recipe_variableselection_correlations.str.

You will need a copy of Microsoft Excel to visualize the correlation matrix.

How to do it...

To remove redundant variables using correlation matrices:

  1. Open the stream, recipe_variableselection_correlations.str by navigating to File | Open Stream.

  2. Make sure the datafile points to the correct path to the file nasadata.txt.

  3. Open the Type node named Correlation Types. Notice that there are several variables of type continuous whose direction values have been set to Input, and a single continuous variable has its direction set to Target. The variable set to Target can be any variable that won't be an input to the model. If you don't have a good candidate, you can create a random variable and set that...