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Topics and Objectives:
Decision Trees are the third approach we will examine in the category of predictive models.
Given a dependent variable and a set of potential predictor variables, decision trees are computationally intensive algorithms that iteratively search through the predictor variables to identify those that are 'best' are predicting the dependent variable. In the process the algorithms produce a 'tree' (hence the name) and also a set of readily interpretable decision rules (for example, if professional female over age 30 with young children, then send offer for educational children's software club membership).
Readings:
Decision Trees
Decision Tree Tutorial (this gets rather involved but tutorials 1-4 offer a pretty good overview, feel free to skim/explore this site)
Target the Right People More Effectively, SPSS white paper
This is a bit of a sales ptich for SPSS's Decision Tree package - but does include examples and applications
Assignment 4: Predicting Response at BookBinders: Decision Trees
This assignment returns to the BookBinders Book Club for one last comparison: how will decision trees compare with RFM and logistic
regression?
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