> > Sent: Tuesday, January 04, 2011 6:30 PM
> > To: r-help@r-project.org
> > Subject: Re: [R] randomForest speed improvements
> >
> >
> > Andy,
> >
> > Thanks for the reply. I had no idea I could combine them
> > back ... that
> >
egrading.
Andy
> -Original Message-
> From: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] On Behalf Of apresley
> Sent: Tuesday, January 04, 2011 6:30 PM
> To: r-help@r-project.org
> Subject: Re: [R] randomForest speed improvements
>
>
> Andy,
&
Andy,
Thanks for the reply. I had no idea I could combine them back ... that
actually will work pretty well. We can have several "worker threads" load
up the RF's on different machines and/or cores, and then re-assemble them.
RMPI might be an option down the road, but would be a bit of overhea
ng only certain quantiles. The current RF code
doesn't do this.
Andy
> -Original Message-
> From: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] On Behalf Of apresley
> Sent: Monday, January 03, 2011 6:28 PM
> To: r-help@r-project.org
> Subject: R
I haven't tried changing the mtry or ntree at all ... though I suppose with
only 6 variables, and tens-of-thousands of rows, we can probably do less
than 500 tree's (the default?).
Although tossing the forest does speed things up a bit, seems to be about 15
- 20% faster in some cases, I need to k
ord... imbue it."
- Jubal Early, Firefly
r-help-boun...@r-project.org wrote on 01/03/2011 02:59:29 PM:
> [image removed]
>
> [R] randomForest speed improvements
>
> apresley
>
> to:
>
> r-help
>
> 01/03/2011 03:03 PM
>
> Sent by:
>
> r-help
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[1:5,1:6];
y<- data202[1:5,8];
y<- y[,drop=TRUE];
x2 <- data202[
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