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

Chapter 10. Decision Tree Models

The previous chapter provided an overview of the various types of data mining models (predictive, clustering, and association) that can be developed in Modeler. Predictive modeling is the most common form of data mining, and as was mentioned in the previous chapter, three very different strategies can be employed: statistical, decision tree, or machine learning. In this chapter, we will discuss:

  • Decision tree theory
  • CHAID theory
  • Partition node
  • CHAID dialog options
  • CHAID results

Before we begin discussing decision tree theory, we'll look at a brief overview of where the next two chapters are going. In this chapter and the next, we are going to build a classification model using Chi-square Automatic Interaction Detection (CHAID). We will then assess its ability to make effective predictions, and finally use the model to score new cases. Our teaching experience has shown us that in order to limit confusion, it is a good idea to briefly revisit the notion of an algorithm...