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IBM SPSS Modeler Essentials
IBM SPSS Modeler Essentials

IBM SPSS Modeler Essentials: Effective techniques for building powerful data mining and predictive analytics solutions

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Profile Icon Keith McCormick Profile Icon Jesus Salcedo
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eBook Dec 2017 238 pages 1st Edition
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Arrow left icon
Profile Icon Keith McCormick Profile Icon Jesus Salcedo
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eBook Dec 2017 238 pages 1st Edition
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IBM SPSS Modeler Essentials

Chapter 2. The Basics of Using IBM SPSS Modeler

The previous chapter introduced the notion of data mining and the CRISP-DM process model. You learned what data mining is, why you would want to use it, and some of the types of questions you could answer with data mining. The rest of this book is going to focus on how you actually do some of the aspects of data mining—reading data, exploring variables, deriving new fields, developing models, and so on. However, before we can get started with these different data mining projects, we first need to become familiar with the software that we will use to work on the data. In this chapter, you will learn the following:

  • Get an overview of the Modeler interface
  • Learn how to build streams
  • Get an introduction to various help options

Introducing the Modeler graphic user interface


IBM SPSS Modeler can be thought of as a data mining workbench that combines multiple tools and technologies to support the data mining process. Modeler allows users to mine data visually on the stream canvas.

The following figure shows the different areas of the Modeler interface:

As you can see, the Modeler interface is comprised of several components, and these are described in the next few pages.

Stream canvas

The stream canvas is the main work area in Modeler. It is located in the center of the Modeler user interface. The stream canvas can be thought of as a surface on which to place icons or nodes. These nodes represent operations to be carried out on the data. Once nodes have been placed on the stream canvas, they can be linked together to form a stream.

Palettes

Nodes (operations on the data) are contained in palettes. The palettes are located at the bottom of the Modeler user interface. Each palette contains a group of related nodes that are...

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...

Modeler stream rules


You may have noticed that in the previous example, we connected the Var. File node to the Table node and this worked fine. However, what if instead we tried to connect the Table node to the Var. File node? Let's try it:

  1. Right-click the Table node.
  2. Select Connect from the Context menu (notice that the Connect option does not exist).

Let's try something different:

  1. Bring a Statistics File node onto the canvas.
  2. Right-click on the Var. File node.
  3. Select Connect from the Context menu.
  4. Click the Statistics File node (notice that you get an error message when you try to connect these two nodes).

The reason we are experiencing these issues is that there are rules for creating Modeler streams.

Modeler streams are typically comprised of three types of nodes: Source, Process, and Terminal nodes. Connecting nodes in certain ways makes sense in the context of Modeler, and other connections are not allowed.

In terms of general rules, streams always start with a Source node (a node from the Sources...

Help options


When using Modeler, at some point we are going to need help. Modeler provides various help options.

Help menu

The most intuitive way to get help is to use the Help menu. As seen in the following figure, the Help menu provides several options:

  • Help Topics takes you to the Help System, where you can search for various topics
  • CRISP-DM Help provides an introduction to the CRISP-DM methodology
  • Application Examples offers a variety of real-life examples of using common data mining techniques for data preparation and modeling
  • Accessibility Help informs users about keyboard alternatives to using the mouse
  • What's This changes the cursor into a question mark and provides information about any Modeler item you select

Dialog help

Perhaps the most useful help option is to use context sensitive help, which is available in whatever dialog box you are currently working on. For example, let's say that you are using the Var. File node and you either did not know how to use this node or you were unfamiliar...

Summary


In this chapter, you learned about the different components of the Modeler graphic user interface. You also learned how to build streams. Finally, you were introduced to various help options.

In the next chapter, we will take a detailed look at how to bring data into Modeler. We will also discuss how to properly set up the metadata for your fields.

 

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Key benefits

  • Get up–and-running with IBM SPSS Modeler without going into too much depth.
  • Identify interesting relationships within your data and build effective data mining and predictive analytics solutions
  • A quick, easy–to-follow guide to give you a fundamental understanding of SPSS Modeler, written by the best in the business

Description

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.

Who is this book for?

This book is ideal for those who are new to SPSS Modeler and want to start using it as quickly as possible, without going into too much detail. An understanding of basic data mining concepts will be helpful, to get the best out of the book.

What you will learn

  • • Understand the basics of data mining and familiarize yourself with Modeler's visual programming interface
  • • Import data into Modeler and learn how to properly declare metadata
  • • Obtain summary statistics and audit the quality of your data
  • • Prepare data for modeling by selecting and sorting cases, identifying and removing duplicates, combining data files, and modifying and creating fields
  • • Assess simple relationships using various statistical and graphing techniques
  • • Get an overview of the different types of models available in Modeler
  • • Build a decision tree model and assess its results
  • • Score new data and export predictions

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Publication date : Dec 26, 2017
Length: 238 pages
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Language : English
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Publication date : Dec 26, 2017
Length: 238 pages
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Language : English
ISBN-13 : 9781788296823
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Table of Contents

11 Chapters
Introduction to Data Mining and Predictive Analytics Chevron down icon Chevron up icon
The Basics of Using IBM SPSS Modeler Chevron down icon Chevron up icon
Importing Data into Modeler Chevron down icon Chevron up icon
Data Quality and Exploration Chevron down icon Chevron up icon
Cleaning and Selecting Data Chevron down icon Chevron up icon
Combining Data Files Chevron down icon Chevron up icon
Deriving New Fields Chevron down icon Chevron up icon
Looking for Relationships Between Fields Chevron down icon Chevron up icon
Introduction to Modeling Options in IBM SPSS Modeler Chevron down icon Chevron up icon
Decision Tree Models Chevron down icon Chevron up icon
Model Assessment and Scoring Chevron down icon Chevron up icon

Customer reviews

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Em Mar 28, 2018
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Great introduction to learning IBM SPSS Modeler and data mining.
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