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 7. Deriving New Fields

We have now spent several chapters on the data preparation phase of data mining. We have learned how to select and sort cases, how to identify and remove duplicate cases, how to reclassify and filter fields, and how to combine different types of data files. In this chapter you will learn how to create additional fields by:

  • Deriving fields as formulas
  • Deriving fields as flags
  • Deriving fields as nominals
  • Deriving fields as conditionals

A very important aspect of every data mining project is to extract as much information as possible. Every project will begin with some data, but it is the responsibility of the data miner to gather additional information from what is already known. In our experience, this can be the most creative and challenging aspect of a data mining project. For example, you might have survey data, but this data might need to be summed for more information, such as a total score on the survey, or the average score on the survey, and so on. In other...