Segmented Regression Estimators for Massive Data Sets

Abstract We describe two methodologies for obtaining segmented regression estimators from massive training data sets. The first methodology, called Linear Regression Tree (LRT), is used for continuous response variables, and the second and complementary methodology, called Naive Bayes Tree (NBT), is used for categorical response variables. These are implemented in the IBM ProbE™ (Probabilistic Estimation) data mining engine, which is an object-oriented framework for building classes of segmented predictive models from massive training data sets. Based on this methodology, an application called ATM-SE™ for direct-mail targeted marketing has been developed jointly with Fingerhut Business Intelligence [1]).