Hello, ? I am working with the na?ve bayes function inlibrary(e1071). ? The function calls are: transactions.train.nb = naiveBayes(as.factor(DealerID) ~ ???????????????????????????????????as.factor(Manufacturer) ??????????????????????????????????? + as.factor(RangeDesc) ??????????????????????????????????? +as.factor(BodyType)? ??????????????????????????????????? +as.factor(FuelType) ??????????????????????????????????? +as.factor(PaintColour) ??????????????????????????????????? +as.factor(TransmissionType) ??????????????????????????????????? +as.factor(Mileage) ??????????????????????????????????? +as.factor(Registration), ?????????????????????????????????????data=transactions.train, ?????????????????????????????????????na.action=na.omit) ? where transactions.train is a dataframe with dimension 2032rows by 14 columns. ? and ? transactions.test.nb = predict(transactions.train.nb,transactions.test[,-1], type='raw') ? An example of the result are View(transactions.test.nb) ? Reduced results shown: ??????????????? 188???????? ????????????225???????? ????????????????229???????? ????????????????270???????? ????????????273 ??????????????? ??????????????? ??????????????? ??????????????? ??????????????? ? 1????????????? 0.000984????????????? 0.000492????????????? 0.000492????????????? 0.000492????????????? 0.001476 2????????????? 0.000984????????????? 0.000492????????????? 0.000492????????????? 0.000492????????????? 0.001476 3????????????? 0.000984????????????? 0.000492????????????? 0.000492????????????? 0.000492????????????? 0.001476 4????????????? 0.000984????????????? 0.000492????????????? 0.000492????????????? 0.000492????????????? 0.001476 5????????????? 0.000984????????????? 0.000492????????????? 0.000492????????????? 0.000492????????????? 0.001476 ? I was struggling to understand why the returnedprobabilities are the same for each column as I was hoping for them to bedifferent. Dealer ID should have a different probability to row 1 than row 2.Each row does sum to 1. ? Transactions.train represents 67% of the full set of data. I?ve tried introducing laplace smoothing, and experimentedwith increasing and decreasing the number of parameters used to generate thetraining naivebayes object But as of yet I can?t figure it out.? Could anybody help? ? Kind regards, Phil, [[alternative HTML version deleted]]