There are a couple of things you may want to try, if you can load the data into R and still have enough to spare:
- Run randomForest() with fewer trees, say 10 to start with. - Run randomForest() with nodesize set to something larger than the default (5 for classification). This puts a limit on the size of the trees being grown. Try something like 21 and see if that runs, and adjust accordingly. HTH, Andy From: Nagu > Hi, > > I am trying to run randomForests on a datasets of size 500000X650 and > R pops up memory allocation error. Are there any better ways to deal > with large datasets in R, for example, Splus had something like > bigData library. > > 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. > > > ------------------------------------------------------------------------------ Notice: This e-mail message, together with any attachme...{{dropped:15}} ______________________________________________ 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.
