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

Decision tree theory

Decision or classification trees are extremely common predictive models that successively partition data based on the relationship between the target and predictor variables. Decision tree models indicate which predictors are most strongly related to the target. These methods are capable of combing through a large set of predictors by successively splitting a dataset into subgroups on the basis of the relationships between predictors and the target field. Decision tree models classify cases into groups and predict values of a target field based on the values of the predictor fields.

Decision trees have some very attractive features:

  • They create segments that are mutually exclusive and exhaustive, meaning that they allow you to identify homogeneous groups
  • They make it easy to construct rules for making predictions about individual cases
  • They can handle a large number of predictors, so that the most important variables are at the top of the tree and the least important variables...