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 > > ______________________________**________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/**listinfo/r-help<https://stat.ethz.ch/mailman/listinfo/r-help> > PLEASE do read the posting guide http://www.R-project.org/** > posting-guide.html <http://www.R-project.org/posting-guide.html> > and provide commented, minimal, self-contained, reproducible code. > -- Max [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
