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

CHAID results

Two panes are visible in the CHAID model window. The pane on the left contains the model in the form of a decision tree. Initially, only the first branch of the tree is visible. The other pane contains a bar chart depicting variable importance, which is a measure that indicates the relative importance of each field in estimating the model. Since the values are relative, the sum of the values for all fields on the display is 1.0. Notice that the Educated_fulltime field is the most important field in this model. The next most important field is Investment, followed by the Sex field. Also notice that only the fields that were used by the model were kept:

The Educated_fulltime field creates the first split in the tree. This information is saying that if Educated_fulltime has a value of No, then mode value for Income_category is less than $50,000. The Mode lists the most frequent category for the branch, and the mode will be the predicted value, unless there are other fields that...