Overview

This course provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values. Students will be exposed to rule induction models such as CHAID and C&R Tree. They will also be introduced to traditional statistical models such as Linear Regression. Students are introduced to machine learning models, such as Neural Networks. Business use case examples include: predicting the length of subscription for newspapers, telecommunication, and job length, as well as predicting insurance claim amounts.

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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.

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Delegates will learn how to

Please refer to course overview

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Outline

1: Introduction to predicting continuous targets
• List three modeling objectives
• List two business questions that involve predicting continuous targets
• Explain the concept of field measurement level and its implications for selecting a modeling technique
• List three types of models to predict continuous targets
• Determine the classification model to use

2: Building decision trees interactively
• Explain how CHAID grows a tree
• Explain how C&R Tree grows a tree
• Build CHAID and C&R Tree models interactively
• Evaluate models for continuous targets
• Use the model nugget to score records

3: Building your tree directly
• Explain the difference between CHAID and Exhaustive CHAID
• Explain boosting and bagging
• Identify how C&R Tree prunes decision trees
• List two differences between CHAID and C&R Tree

4: Using traditional statistical models
• Explain key concepts for Linear
• Customize options in the Linear node
• Explain key concepts for Cox
• Customize options in the Cox node

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

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Dates & Locations

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