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

Reclassifying categorical values

In the previous chapter, we used the Data Audit node to investigate the data file. The most critical problem that was found was that children were included in the dataset; the Select node was used to fix this issue. However, the Data Audit node also revealed additional irregularities within the data. For example, the Education field had too many categories, several of which had very low counts. In addition, the Vet_benefits field, which is a categorical variable, was coded with numeric values, so adding labels made interpretation easier.

Also, the Household_summary field had a misspelled version of a category that was creating an additional unnecessary category. The birth country fields needed to be transformed, given the low counts on most of the categories. We use the Reclassify node to fix all these problems.

The Reclassify node allows you to reclassify or recode the data values for categorical fields. For example, let's say that customers reported satisfaction...