<p>a couple of hours seems a lot of time. Are you sure you are running on an optimized machine? Perhaps you could experiment on Linux and AWS EC2. Also check out <code>ranger</code> which has been out since a couple of weeks <a href="http://arxiv.org/abs/1508.04409" rel="nofollow noreferrer">http://arxiv.org/abs/1508.04409</a> and
<a href="https://cran.r-project.org/web/packages/ranger/index.html" rel="nofollow noreferrer">https://cran.r-project.org/web/packages/ranger/index.html</a></p>

<p>Also check <a href="https://stackoverflow.com/questions/14106010/parallel-execution-of-random-forest-in-r">parallel execution of random forest in R</a></p>
Using combine() with the R Package randomForest
<p>I'm working with a very large set of data, about 120,000 rows and 34 columns. As you can well image, when using the R package randomForest, the program takes quite a number of hours to run, even on a powerful Windows server.  </p>

<p>Although I am no expert in randomForest, I have a question about the proper use of the combine() function.  </p>

<p>I seem to get conflicting answers when I researched this question online.  Some say that you can only use combine() when using randomForest on the same set of data.  Others say that you can just use combine().</p>

<p>What I'd like (hope, dream) to do is break up the 120,000 rows of data into 6 data frames, each containing 20,000 rows and perform randomForest on each of the 6 data frames.  My hope is that I can use the combine() function to then combine the results of all 6 together.  Is that possible?  </p>

<p>Any help in this matter would be greatly appreciated.  </p>