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Predicting Continuous Targets Using IBM SPSS Modeler (V16)


Course Description


Overview

Predicting Continuous Targets Using IBM SPSS Modeler (v16) is an intermediate level course that 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. Machine learning models will also be presented. Business use case examples include: predicting the length of subscription (for newspapers, telecommunication, job length, and so forth) and predicting claim amount (insurance).  

Audience

This intermediate course is for IBM SPSS Modeler Analysts who have completed the Introduction to IBM SPSS Modeler and Data Mining course who want to become familiar with the modeling techniques available in IBM SPSS Modeler to predict a continuous target.

Prerequisites

You should have:
  • Completed Introduction to IBM SPSS Modeler and Data Mining (V16)
  • Experience using IBM SPSS Modeler, including familiarity with the IBM SPSS Modeler environment, creating streams, importing data (Var. File node), basic data preparation (Type node, Derive node, Select node), reporting (Table node, Data Audit node), and creation of models

Key topics

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
Building Your Tree 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
Building Your Tree Directly
  • Customize two options in the CHAID node
  • Customize two options in the C&R Tree node
  • List one difference between CHAID and C&R Tree
Using Traditional Statistical Models
  • Explain key concepts for Linear
  • Customize one option in the Linear node
  • Explain key concepts for Cox
  • Customize one option in the Cox node
Using Machine Learning Models
  • Explain key concepts for Neural Net
  • Customize one option in the Neural Net node

Objectives

Please refer to course overview for description information.

Course Duration

1 day
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