thx for your help, i checked the caret package out and the tuning works. but i can't find a way to make a contingency table in order to see the classification result.
e.g. like: table(outcome NaiveBayes, mydata$code) Is there something like that? Julia -------- Original-Nachricht -------- > Datum: Tue, 30 Oct 2007 17:03:49 -0400 > Von: "Kuhn, Max" <[EMAIL PROTECTED]> > An: "Julia Kröpfl" <[EMAIL PROTECTED]>, r-help@r-project.org > Betreff: RE: [R] NAIVE BAYES with 10-fold cross validation > > am trying to implement the code of the e1071 package for naive bayes, > > but it doens't really work, any ideas?? > > am very glad about any help!! > > need a naive bayes with 10-fold cross validation: > > The caret package will do this. Use > > fit <- train( > x, y, method = "nb", > trControl = trainControl(method = "cv", number = 10)) > > (there is no formula interface yet). > > It will use the naïve Bayes implementation in klaR. Unless you specify > otherwise, it will train naïve Bayes models with and without using kernel > density estimation (but you can change that). > > The object fit$finalModel will contain the model fit that is "cv optimal". > > For example: > > > fit <- train( > + iris[,-5], iris$Species, "nb", > + trControl = trainControl(method = "cv", number = 10)) > Iter 1 Values: TRUE > Loading required package: MASS > Loading required package: class > Iter 2 Values: FALSE > > > > fit > > Call: > train.default(x = iris[, -5], y = iris$Species, method = "nb", > trControl = trainControl(method = "cv", number = 10)) > > 150 samples > 4 predictors > > summary of cross-validation (10 fold) sample sizes: > 135, 135, 135, 135, 135, 135, ... > > cv resampled training results across tuning parameters: > > usekernel Accuracy Kappa Accuracy SD Kappa SD Optimal > FALSE 0.953 0.93 0.0706 0.106 > TRUE 0.96 0.94 0.0562 0.0843 * > > Accuracy was used to select the optimal model > > > Max > > > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > On Behalf Of "Julia Kröpfl" > Sent: Tuesday, October 30, 2007 4:46 PM > To: r-help@r-project.org > Subject: [R] NAIVE BAYES with 10-fold cross validation > > hi there!! > > i am trying to implement the code of the e1071 package for naive bayes, > but it doens't really work, any ideas?? > i am very glad about any help!! > i need a naive bayes with 10-fold cross validation: > > code: > library(e1071) > > model <- naiveBayes(code ~ ., mydata) > > tune.control <- tune.control(random = FALSE, nrepeat = 1, repeat.aggregate > = min, > sampling = c("cross"), sampling.aggregate = mean, > cross = 10, best.model = TRUE, performances = TRUE) > > pred <- predict(model, mydata[,-12], type="class") > tune(naiveBayes, code~., mydata, predict.fun=pred, tune.control) > > > thx for your help! > cheers, julia > -- > > ______________________________________________ > 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. -- Psssst! Schon vom neuen GMX MultiMessenger gehört? Der kann`s mit allen: http://www.gmx.net/de/go/multimessenger ______________________________________________ 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.
