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

Relationships between categorical and continuous fields

When working with categorical variables, frequency tables and graphs containing counts and percentages are appropriate summaries. For continuous variables, because there are typically so many unique values, that counts and percentages can quickly become less useful and unwieldy, so traditionally we turn to the mean as the summary statistic of choice, since it provides a single measure of central tendency. In this section, we will discuss two nodes, the Histogram and Means nodes, that allow you to investigate the distribution and relationships of continuous and categorical variables.

Histogram node

The Histogram node shows the distribution of values for continuous fields. To use the Histogram node:

  1. Place a Histogram node on the canvas.
  2. Connect the Type node to the Histogram node.
  3. Edit the Histogram node.

To see the distribution of a continuous field, place a field in the Field drop-down list:

  1. Click the Field drop-down arrow.
  2. Select the field...