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
Thank you Andy.
It is throwing memory allocation error for me for numerous
combinations of ntree and nodesize values. I tried with memory.limit()
and memory.size to use the maximum memory but the error was
consistent. But one thing I noticed was that I had tough time even
just loading the dataset
Also, use the non-formula interface to the function:
# saves some space
randomForest(x, y)
the formula interface:
# avoid:
randomForest(y~., data = something)
This second method saves a terms object that is very sparse and takes
up a lot of space.
Max
On Wed, Feb 27, 2008 at 12:31
Hi,
I am trying to run randomForests on a datasets of size 50X650 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
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