[R] randomForest speed improvements

[apresley]

Hi there,

We're trying to use randomForest to do some predictions.  The test-harness
for our code is pretty straightforward:

  library ('randomForest');
  data202 <- read.csv ("random.csv", header=TRUE);
  x<- data202[1:50000,1:6];
  y<- data202[1:50000,8];
  y<- y[,drop=TRUE];

  x2 <- data202[50001:60000,1:6];
  y2 <- data202[50001:60000,8];
  y2 <- y2[,drop=TRUE];

  RFobject <- randomForest(x,y,na.action=na.roughfix);
  p <- predict (RFobject, x2);

In this case, the CSV contains 10 columns, of which 1-6 are numeric in
nature (day of week, week of month, etc...) and column 8 is the target
(sales, a numeric number).

randomForest does fine with the data, our issue is how long it takes.  In
this case, about 5,000 rows of data seems to take just a few seconds, but
going to 50,000 rows doesn't take 5x the time, it takes perhaps 30 or 40
minutes.

We've downloaded and tried RT-Rank, which is a multi-threaded version of
RandomForest, and this seems to produce the same (or slightly better)
predictions, but also gets done fairly quickly.

What can we do to improve the speed of this data computation?  The system
we're on is a dual quad-core Intel CPU @ 2.33Ghz, and with 16GB of RAM ...
we're using the "stock" R RPM for CentOS 5.5.

Thanks!

--
Anthony
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