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

Contrasting model assessment with the Evaluation phase

As we briefly discussed in Chapter 1Introduction to Data Mining and Predictive Analytics, model assessment (a modeling phase task) is quite different from the Evaluation phase. Some of the same tools can apply to both, but the stage of the project and the thought process is quite different. During model assessment, you are potentially comparing a large number of models. You may even try dozens of variations of algorithms, settings, and modifications to the data.

Therefore, you need easy, objective criteria on which to rank these models. Our colleagues and management simply will not have the time to be brought in to judge the efficacy of dozens of models so we need to narrow it down to just a couple of models before the Evaluation phase begins. Tom Khabaza, one of the original authors of CRISP-DM has written the Nine Laws of Data Mining ( and the 8th Law of Data Mining is the Value...