Hi Pierre, Thanks a lot for your help.. So, using that script, I just separate my data in two parts, right? For using as training set the 70 % of the data and the rest as test, should I multiply the n with the 0.70 (for this case)?
Many thanks, Chrysanthi 2009/4/12 Pierre Moffard <pier.m...@yahoo.fr> > 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-h...@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.
