Book Image

IBM SPSS Modeler Essentials

By : Jesus Salcedo, Keith McCormick
Book Image

IBM SPSS Modeler Essentials

By: Jesus Salcedo, Keith McCormick

Overview of this book

IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data. With almost 25 years of history, Modeler is the most established and comprehensive Data Mining workbench available. Since it is popular in corporate settings, widely available in university settings, and highly compatible with all the latest technologies, it is the perfect way to start your Data Science and Machine Learning journey. This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler’s easy to learn “visual programming” style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions. Using a single case study throughout, this intentionally short and focused book sticks to the essentials. The authors have drawn upon their decades of teaching thousands of new users, to choose those aspects of Modeler that you should learn first, so that you get off to a good start using proven best practices. This book provides an overview of various popular data modeling techniques and presents a detailed case study of how to use CHAID, a decision tree model. Assessing a model’s performance is as important as building it; this book will also show you how to do that. Finally, you will see how you can score new data and export your predictions. By the end of this book, you will have a firm understanding of the basics of data mining and how to effectively use Modeler to build predictive models.
Table of Contents (19 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

CHAID theory

CHAID is a decision tree model. It is one of the oldest and most popular data mining techniques. At its core, CHAID is based on the Chi-square test; it chooses the predictors that have the strongest interaction (give the largest Chi-square statistic) with the target field and then it divides the sample based on this predictor. It can use the Pearson or Likelihood ratio Chi-square statistic. In the figure shown next, notice that the Educated_fulltime field is the most important predictor, because this is the field that has the largest Chi-square statistics. In fact, notice that the Chi-square statistic and percentages are exactly the same for both the CHAID model and the Chi-square test. In addition, if the categories of the predictor do not differ significantly from each other, the categories will be merged together. CHAID generates non-binary trees so it tends to create wider trees:

How CHAID processes different types of input variables

When using CHAID, or any algorithm, it...