Random forest on a big dataset

Question

I have a large dataset in R (1M+ rows by 6 columns) that I want to use to train a random forest (using the randomForest package) for regression purposes. Unfortunately, I get a Error in matrix(0, n, n) : too many elements specified error when trying to do the whole thing at once and cannot allocate enough memory kind of errors when running in on a subset of the data -- down to 10,000 or so observations.

Seeing that there is no chance I can add more RAM on my machine and random forests are very suitable for the type of process I am trying to model, I'd really like to make this work.

Any suggestions or workaround ideas are much appreciated.

Run with proximity = FALSE as joran suggested and tell us if it works. — smci
One relatively simple way around your problem would be to subset your input matrix. All that data probably won't give you a better model than one with a subset of size 10K x 10K. — Tim Biegeleisen
Did you have a look at library(h2o) ? That runs OK for very large problems, see r-bloggers.com/benchmarking-random-forest-implementations — Tom Wenseleers

joran joran · Accepted Answer · 2012-04-06T03:44:49

You're likely asking randomForest to create the proximity matrix for the data, which if you think about it, will be insanely big: 1 million x 1 million. A matrix this size would be required no matter how small you set sampsize. Indeed, simply Googling the error message seems to confirm this, as the package author states that the only place in the entire source code where n,n) is found is in calculating the proximity matrix.

But it's hard to help more, given that you've provided no details about the actual code you're using.

Random forest on a big dataset

1 Answers