Thank you Uwe Ligges. Yes. I had only 50 trees. I come across memory problems running for big number of trees. Also, I am going to post my next question in a separate thread, but, it does not harm me to ask here. How do I deal with large datasets when using randomForests. I have approximately, datasets of size 500000X650, and R just can't deal with it (pops up memory allocation problems). Are there any better ways to deal with large datasets in R, for example, Splus had something like bigData library.
Thank you, Nagu On Mon, Feb 25, 2008 at 1:56 AM, Uwe Ligges <[EMAIL PROTECTED]> wrote: > > > > Nagu wrote: > > Hi, > > > > I am using randomForests for a classification problem. The predict > > function in the randomForest library, when asked to return the > > probabilities, has precision of two digits after the decimal. I need > > at least four digits of precision for the predicted probabilities. How > > do I achieve this? > > For me it gives the desired precision, adapting the > ?predict.randomForest example: > > data(iris) > set.seed(111) > ind <- sample(2, nrow(iris), replace = TRUE, prob=c(0.8, 0.2)) > iris.rf <- randomForest(Species ~ ., data=iris[ind == 1,], ntree = 2000) > iris.pred <- predict(iris.rf, iris[ind == 2,], type = "prob") > iris.pred > > Maybe you do not have much more than 1000 trees in your bag? > > Uwe Ligges > > > > > > > > > > > Thank you, > > Nagu > > > > ______________________________________________ > > 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. > ______________________________________________ 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.
