Generalized Naive Bayes Classifiers Kim Larsen   View Paper (PDF)   Return to June 2005, Volume 7, Issue 1 Abstract This paper presents a generalization of the Naive Bayes Classifier. The method is specifically designed for binary classification problems commonly found in credit scoring and marketing applications. The Generalized Naive Bayes Classifier turns out to be a powerful tool for both exploratory and predictive analysis. It can generate accurate predictions through a flexible, non-parametric fitting procedure, while being able to uncover hidden patterns in the data. In this paper, the Generalized Naive Bayes Classifier and the original Bayes Classifier will be demonstrated. Also, important ties to logistic regression, the Generalized Additive Model (GAM), and Weight Of Evidence will be discussed. ©2006 Association for Computing Machinery