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


The main idea behind Classification is that you are trying to predict or understand one variable by making use of other variables. For example, you may be trying to predict which customers are likely to apply for a credit card, and you are trying to predict this from the customers' demographic and financial variables. Hence, in this scenario there are two types of variables:

  • The target variable: This is a variable that you are trying to predict or understand
  • The input variables: These are variables that you are using to try to predict or understand the target variable

The ultimate goal of this type of analysis is predictive accuracy. In this course, we will only have time to cover a couple of predictive models. Modeler offers a great number of predictive models. This section gives an overview, distinguishing between three classes of classification models, which are listed as follows:

  • Rule induction models
  • Statistical models
  • Machine learning models

Rule induction models are an important...