Predictive Modeling for Continuous Targets Using IBM SPSS Modeler v18

Duration: 
1 days
Codes: 
0A0V7GAU,IBM,SPSS
Versions: 
V18

Overview

This course (formerly Predicting Continuous Targets Using IBM SPSS Modeler (v16)) 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

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

Please refer to course overview.

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.

Course 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

Thinking about Onsite?

If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. It's a cost effective option.

Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.

Upcoming Dates

  • GREEN This class is Guaranteed To Run.
  • SPVC - Self-Paced Virtual Class.
  • Click a Date to Enroll.
Course Location Days Cost Date
Onsite
Onsite1 500 £500 2019-01-15
Midlands
Birmingham1 500 £500 2019-02-15
Manchester
Manchester1 500 £500 2019-02-15
Glasgow
Glasgow1 500 £500 2019-02-15