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

Data Audit node options

When data is first read into Modeler, it is important to check the data to make sure it was read correctly. Typically, using a Table node can help you get a sense of the data and inform you of some potential issues that you may have. However, the Data Audit node is a better alternative to using a Table node, as it provides a more thorough look at the data.

Before modeling takes place, it is important to see how records are distributed within the fields in the dataset. Knowing this information can identify values that, on the surface, appear to be valid, but when compared to the rest of the data are either out of range or inappropriate. Let's begin by opening a stream that has the modifications we made in the previous chapter:

  1. Open the Data Quality and Exploration stream.


This simple stream contains the Demographic data file that has been linked to the Var. File source, along with the modifications we previously made in the Types tab. In order for this stream to function...