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Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18.1.1)

Course content updated by LearnQuest
Price
525 - 815 USD
8 Hours
0A0U8G
Classroom Training, Online Training
IBM Business Partner
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  • Learn on Demand
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 525 USD
  • Date: 31-Jan-2022
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 815 USD
  • Date: 8-Feb-2022
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 815 USD
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Course Description

Overview

This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

Objectives

1: Introduction to predictive models for categorical targets 
• Identify three modeling objectives 
• Explain the concept of field measurement level and its implications for selecting a modeling technique 
• List three types of models to predict categorical targets 
 

2: Building decision trees interactively with CHAID 
• Explain how CHAID grows decision trees 
• Build a customized model with CHAID 
• Evaluate a model by means of accuracy, risk, response and gain 
• Use the model nugget to score records 
 

3: Building decision trees interactively with C&R Tree and Quest 
• Explain how C&R Tree grows a tree 
• Explain how Quest grows a tree 
• Build a customized model using C&R Tree and Quest 
• List two differences between CHAID, C&R Tree, and Quest

 

4: Building decision trees directly 
• Customize two options in the CHAID node 
• Customize two options in the C&R Tree node 
• Customize two options in the Quest node 
• Customize two options in the C5.0 node 
• Use the Analysis node and Evaluation node to evaluate and compare models 
• List two differences between CHAID, C&R Tree, Quest, and C5.0 
 

5: Using traditional statistical models 
• Explain key concepts for Discriminant 
• Customize one option in the Discriminant node 
• Explain key concepts for Logistic 
• Customize one option in the Logistic node 
 

6: Using machine learning models 
• Explain key concepts for Neural Net 
• Customize one option in the Neural Net node

Audience

• Analytics business users who have completed the Introduction to IBM SPSS Modeler and Data Mining course and who want to become familiar with analytical models to predict a categorical field (yes/no churn, yes/no fraud, yes/no response to a mailing, pass/fail exams, yes/no machine break-down, and so forth).

Prerequisites

    • Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and a basic knowledge of modeling.
    • Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1) is recommended.
     

Topics

1: Introduction to predictive models for categorical targets
• Identify three modeling objectives
• Explain the concept of field measurement level and its implications for selecting a modeling technique
• List three types of models to predict categorical targets

2: Building decision trees interactively with CHAID
• Explain how CHAID grows decision trees
• Build a customized model with CHAID
• Evaluate a model by means of accuracy, risk, response and gain
• Use the model nugget to score records

3: Building decision trees interactively with C&R Tree and Quest
• Explain how C&R Tree grows a tree
• Explain how Quest grows a tree
• Build a customized model using C&R Tree and Quest
• List two differences between CHAID, C&R Tree, and Quest

4: Building decision trees directly
• Customize two options in the CHAID node
• Customize two options in the C&R Tree node
• Customize two options in the Quest node
• Customize two options in the C5.0 node
• Use the Analysis node and Evaluation node to evaluate and compare models
• List two differences between CHAID, C&R Tree, Quest, and C5.0

5: Using traditional statistical models
• Explain key concepts for Discriminant
• Customize one option in the Discriminant node
• Explain key concepts for Logistic
• Customize one option in the Logistic node

6: Using machine learning models
• Explain key concepts for Neural Net
• Customize one option in the Neural Net node

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Learn at your own pace with anytime, anywhere training

  • Same in-demand topics as instructor-led public and private classes.
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Self-Paced Training Terms & Conditions

THIS IS A SELF-PACED VIRTUAL CLASS. AFTER YOU REGISTER, YOU HAVE 30 DAYS TO COMPLETE THE COURSE.

This is a Self-Paced virtual class; it is intended for students who do not need the support of a classroom instructor. If you feel you would better benefit from having access to a Subject Matter Expert, please enroll in the Instructor-Led version instead. Minimal technical support is provided to address issues with accessing the platform or problems within the lab environment.

Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.

After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.

You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.

If the course requires a remote lab system, the lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system.

Click the Skytap Connectivity Test button to ensure this computer's hardware, software and internet connection works with the SPVC Lab Environment.

Click the Skytap Connectivity Documentation button to read about the hardware, software and internet connection requirements.

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