Thanks a lot for the quick answer.
However, from what I see, the parallelization affects only the
cross-validation part in the gbm interface (but it changes nothing when
you call gbm.fit).
Am I missing anything here?
Is there any fundamental reason why gbm.fit cannot be parallelized?
Lorenzo
On Sun, 24 Mar 2013 12:45:39 +0100, Max Kuhn <mxk...@gmail.com> wrote:
See this:
https://code.google.com/p/gradientboostedmodels/issues/detail?id=3
and this:
https://code.google.com/p/gradientboostedmodels/source/browse/?name=parallel
Max
On Sun, Mar 24, 2013 at 7:31 AM, Lorenzo Isella
<lorenzo.ise...@gmail.com> wrote:
Dear All,
I am far from being a guru about parallel programming.
Most of the time, I rely or randomForest for data mining large datasets.
I would like to give a try also to the gradient boosted methods in GBM,
but I have a need for parallelization.
I normally rely on gbm.fit for speed reasons, and I usually call it
this way
gbm_model <- gbm.fit(trainRF,prices_train,
offset = NULL,
misc = NULL,
distribution = "multinomial",
w = NULL,
var.monotone = NULL,
n.trees = 50,
interaction.depth = 5,
n.minobsinnode = 10,
shrinkage = 0.001,
bag.fraction = 0.5,
nTrain = (n_train/2),
keep.data = FALSE,
verbose = TRUE,
var.names = NULL,
response.name = NULL)
Does anybody know an easy way to parallelize the model (in this case it
means simply having 4 cores on the same >>machine working on the
problem)?
Any suggestion is welcome.
Cheers
Lorenzo
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Max
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and provide commented, minimal, self-contained, reproducible code.
