Predictive Modeling for Continuous Targets Using IBM SPSS Modeler v18.1.1 0A0V8G

Duration: 
1 days
Codes: 
0A0V8G
Versions: 
V18.1.1

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.

Audience

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.

Skills Gained

  • 1: Introduction to predicting continuous targets
  • 2: Building decision trees interactively
  • 3: Building your tree directly
  • 4: Using traditional statistical models
  • 5: Using machine learning models

Course Outline

1: Introduction to predictive models for 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 decision trees 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

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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