Hi, Currently I testing the packets that contain built-in features for classification. Actually I looked packages such as: e1071, Klar, Caret, CORElearn. However, from what I noticed when building a naive Bayesian classifier, that they package use of the finite mixture model to estimate P (x | C) and using a normal distribution. In my research I use binary data and I want modeled P (x | C), eg the Poisson distribution. Are the packages in the r-project that allows for replacing kernel to estimate P (x | C) as another distribution (the http://www.statsoft.com/textbook/naive-bayes-classifier/)? Or I must implement such a solution yourself?
Best Marcin M. -- View this message in context: http://r.789695.n4.nabble.com/Naive-Bayes-Classifier-tp3652658p3652658.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
