Hi Chysanthi, check out the randomForest package, with the function randomForest. It has a CV option. Sorry for not providing you with a lengthier response at the moment but I'm rather busy on a project. Let me know if you need more help.
Also, to split your data into two parts- the training and the test set you can do (n the number of data points): n<-length(data[,1]) indices<-sample(rep(c(TRUE,FALSE),each=n/2),round(n/2),replace=TRUE) training_indices<-(1:n)[indices] test_indices<-(1:n)[!indices] Then, data[train,] is the training set and data[test,] is the test set. Best, Pierre ________________________________ De : Chrysanthi A. <chrys...@gmail.com> À : r-help@r-project.org Envoyé le : Dimanche, 12 Avril 2009, 17h26mn 59s Objet : [R] Running random forest using different training and testing schemes Hi, I would like to run random Forest classification algorithm and check the accuracy of the prediction according to different training and testing schemes. For example, extracting 70% of the samples for training and the rest for testing, or using 10-fold cross validation scheme. How can I do that? Is there a function? Thanks a lot, Chrysanthi. [[alternative HTML version deleted]] ______________________________________________ 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. [[alternative HTML version deleted]]
______________________________________________ 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.