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

Evaluating the use of sampling for speed


Modern data mining practice is somewhat different from the ideal. Data miners certainly do develop valuable models that are used in the business and many have massive resources of data to mine, even more data than might have been foreseen a generation ago. But not all data miners meet the profile of a business user, someone whose primary work responsibility is not data analysis and who is not trained in, or concerned with, statistical methods. Nor does the modern data miner shy away from sampling.

In practice, it has been difficult to make discoveries and build models quickly when working with massive quantities of data. Although data mining tools may be designed to streamline the process, it still takes longer for each operation to complete on a large amount of data than it would with a smaller quantity. This sampling can be extremely useful.

Getting ready

We will start with a blank stream, and will be using the cup98lrn reduced vars2.txt data set.

How...