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

Building streams

As was mentioned previously, Modeler allows users to mine data visually on the stream canvas. This means that you will not be writing code for your data mining projects; instead you will be placing nodes on the stream canvas. Remember that nodes represent operations to be carried out on the data. So once nodes have been placed on the stream canvas, they need to be linked together to form a stream. A stream represents the flow of data going through a number of operations (nodes). The following diagram is an example of nodes on the canvas, as well as a stream:

Given that you will spend a lot of time building streams, in this section you will learn the most efficient ways of manipulating nodes to create a stream.

Mouse buttons

When building streams, mouse buttons are used extensively so that nodes can be brought onto the canvas, connected, edited, and so on. When building streams within Modeler, mouse buttons are used in the following ways:

  • The left button is used for selecting...