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.isella@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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