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Abstract:
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Not so long ago, modelers would use traditional classification, data mining, and decision tree techniques to identify a target population. We have come a long way in recent years. By incorporating modern approaches-including boosting, bagging, and netlift-there has been a giant leap in this arena. We will discuss recent improvements to conventional decision tree and logistic regression technology via two case study examples: one in direct marketing and the second in biomedical data analysis. Within the context of real-world examples, we will illustrate the evolution of classification by contrasting and comparing regularized logistic regression, CART, random forests, TreeNet stochastic gradient boosting, and RuleLearner. All attendees will receive six months access to fully functional versions of the SPM Salford Predictive Modeler software suite.
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