IBM SPSS Modeler - Predictive Modeling for Continuous Targets

( Duration: 1 Day )

The IBM SPSS Modeler - Predictive Modeling for Continuous Targets training course provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values. Attendees 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. Attendees are introduced to machine learning models, such as Neural Networks.

  • 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.
  • Attend IBM SPSS Modeler - Data Science course or have equivalent experience.
  • This IBM SPSS Modeler - Predictive Modeling for Continuous Targets workshop is ideal for: SPSS Modeler Analysts who want to become familiar with the modeling techniques available in IBM SPSS Modeler to predict a continuous target.



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 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

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

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

Using machine learning models

  • Explain key concepts for Neural Net
  • Customize one option in the Neural Net node

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