Book Image

IBM SPSS Modeler Cookbook

By : Keith McCormick, Abbott
Book Image

IBM SPSS Modeler Cookbook

By: Keith McCormick, Abbott

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 (11 chapters)
10
Index

Introduction

Given the obvious importance of Modeling, why only one Chapter? Certainly, one could easily write 1000 pages on the various algorithms and the proof is in the large number of books that have done just that. The goal of this Cookbook, however, is to direct the reader to areas that they might otherwise spend too little time on, or to suggest approaches that are non-obvious.

There is much about the many algorithms that is non-obvious. They demand study. Thankfully, they also reward that study but in ways that can be frustrating to the intermediate-level data miner. It is often said, and can actually be shown, that detailed study of a handful of algorithms might be superior than spreading one's professional development time across all of them. It is worth noting that those that have the time and attention to learn R, which would also reward study, could learn hundreds of classifiers, and many hundreds of algorithms. The problem is that while mastering algorithms comes slowly...