<p>The other two answers are good. Another option is to actually use more recent packages that are purpose-built for highly dimensional / high volume data sets. They run their code using lower-level languages (C++ and/or Java) and in certain cases use parallelization.</p>
<p>I'd recommend taking a look into these three:</p>
<p>ranger (uses C++ compiler)
randomForestSRC (uses C++ compiler)
h2o (Java compiler - needs Java version 8 or higher)
Also, some additional reading here to give you more to go off on which package to choose: <a href="https://arxiv.org/pdf/1508.04409.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1508.04409.pdf</a></p>
<p>Page 8 shows benchmarks showing the performance improvement of ranger against randomForest against growing data size - ranger is WAY faster due to linear growth in runtime rather than non-linear for randomForest for rising tree/sample/split/feature sizes.</p>
<p>Good Luck!</p>
Get randomForest regression faster in R
<p>I have to make a regression with randomforest in R. My problem is that my dataframe is huge: I have 12 variables and more than 400k entries. When I try - the code is written in the bottom - to get a randomForest regression the system takes many hours to process the data: after 5, 6 hours of calculation, I am obliged to stop the operation without any output. Someone can suggests me how I can get it faster?
Thanks</p>

<pre><code>library(caret)
library(randomForest)

dataset &lt;- read.csv("/home/anonimo/Modelli/total_merge.csv", header=TRUE)
dati &lt;- data.frame(dataset)
attach(dati)


trainSet &lt;- dati[2:107570,]
testSet &lt;- dati[107570:480343,]

output.forest &lt;- randomForest(dati$Clip_pm25 ~ dati$e_1 + dati$Clipped_so + dati$Clip_no2 + dati$t2m_1 + dati$tp_1 + dati$Clipped_nh  +  dati$Clipped_co + dati$Clipped_o3 + dati$ssrd_1 + dati$Clipped_no + dati$Clip_pm10 + dati$sp_1, data=trainSet, ntree=250)
</code></pre>