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

Creating bagged logistic regression models


Many modeling algorithms in Modeler have Bagging and Boosting options already built-in. However, some models do not, including Logistic Regression. Even for these algorithms that do not have Bagging and Boosting, these model ensembles can help predictive accuracy significantly. In this recipe we learn how to build a bagged ensemble of logistic regression models from 10 bootstrap samples.

Getting ready

This recipe uses the datafile cup98lrn_reduced_vars3.sav and the stream Recipe – bootstrap ensemble.str.

How to do it...

To create bagged logistic regression models:

  1. Open the stream Recipe – bootstrap ensemble.str by navigating to File | Open Stream.

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

  3. Locate the supernode, Bootstrap Sample, select it with a left-click, and copy it by using Edit | Copy or by typing the shortcut Ctrl + C.

  4. Paste the supernode to the stream by using Edit | Paste or by typing the shortcut Ctrl + V....