title: Suggestions for speeding up Random Forests
body: <p>I'm doing some work with the <code>randomForest</code> package and while it works well, it can be time-consuming.  Any one have any suggestions for speeding things up?  I'm using a Windows 7 box w/ a dual core AMD chip.  I know about R not being multi- thread/processor, but was curious if any of the parallel packages (<code>rmpi</code>, <code>snow</code>, <code>snowfall</code>, etc.) worked for <code>randomForest</code> stuff.  Thanks.</p>
  
  <p>EDIT:</p>
  
  <p>I'm using rF for some classification work (0's and 1's).  The data has about 8-12 variable columns and the training set is a sample of 10k lines, so it's decent size but not crazy.  I'm running 500 trees and an mtry of 2, 3, or 4.</p>
  
  <p>EDIT 2: 
  Here's some output:</p>
  
  <pre><code>&gt; head(t22)
    Id Fail     CCUse Age S-TFail         DR MonInc #OpenLines L-TFail RE M-TFail Dep
  1  1    1 0.7661266  45       2 0.80298213   9120         13       0  6       0   2
  2  2    0 0.9571510  40       0 0.12187620   2600          4       0  0       0   1
  3  3    0 0.6581801  38       1 0.08511338   3042          2       1  0       0   0
  4  4    0 0.2338098  30       0 0.03604968   3300          5       0  0       0   0
  5  5    0 0.9072394  49       1 0.02492570  63588          7       0  1       0   0
  6  6    0 0.2131787  74       0 0.37560697   3500          3       0  1       0   1
  &gt; ptm &lt;- proc.time()
  &gt; 
  &gt; RF&lt;- randomForest(t22[,-c(1,2,7,12)],t22$Fail
  +                    ,sampsize=c(10000),do.trace=F,importance=TRUE,ntree=500,,forest=TRUE)
  Warning message:
  In randomForest.default(t22[, -c(1, 2, 7, 12)], t22$Fail, sampsize = c(10000),  :
    The response has five or fewer unique values.  Are you sure you want to do regression?
  &gt; proc.time() - ptm
     user  system elapsed 
   437.30    0.86  450.97 
  &gt; 
  </code></pre>