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

Derive – Formula

Deriving a field as a Formula is extremely common, especially when working with continuous fields. Some of the most common methods of deriving a field as a formula include creating total scores, change scores and ratios. Our data file does not contain many continuous fields, however we do have several income related fields (capital gains, capital losses, and dividends) in the dataset that might be able to shed insight. Clearly, since we are predicting income, we cannot use the actual fields, capital gains, capital losses, or dividends, because all of these fields contribute to a person's overall income, and including them would create a biased model. However, rather than using actual investment dollars, we could investigate if having investments relates to income. Therefore, in order to determine if someone has investments, we first need to create a temporary field, which we will call Stock_numbers, which simply adds up capital gains, capital losses, and dividends. If someone...