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

Levels of measurement and roles

As was mentioned previously, the Types tab is the most important tab within any source node. Let's take a closer look at the Types tab:

  1. Edit the Var. File node.
  2. Click on the Types tab.

The Field column displays each field's name. The Measurement column determines how Modeler will use each field. Initially, fields with numeric values are typed as Continuous, and fields with string values are typed as Categorical. It is important to make sure that when you are initially reading in data, you only have fields with the measurement levels Continuous or Categorical, if this is not the case, you can manually change this.

The current display is based on 50 lines of data and thus presents partially instantiated measurement levels. Measurement levels are fully instantiated when all data passes through the node.

Instantiation refers to the process of reading or specifying information, such as the measurement level and values for a field:

To instantiate the data:

  1. Click Read Values...