Segmented Regression Estimators for Massive Data Sets.
作者： Edwin P. D. Pednault , Ramesh Natarajan
DOI:
关键词: Proper linear model 、 Computer science 、 Nonparametric regression 、 Logistic model tree 、 Polynomial regression 、 Categorical variable 、 Data mining 、 Local regression 、 Regression diagnostic 、 Segmented regression
摘要: We describe two methodologies for obtaining segmented regression estimators from massive training data sets. The first methodology, called Linear Regression Tree (LRT), is used continuous response variables, and the second complementary Naive Bayes (NBT), categorical variables. These are implemented in IBM ProbE (Probabilistic Estimation) mining engine, which an object-oriented framework building classes of predictive models Based on this application ATM-SETM direct-mail targeted marketing has been developed jointly with Fingerhut Business Intelligence [1]).
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