Yes, I think the second link is a test build of a parallelized cv loop within gbm().
On Mar 24, 2013, at 9:28 AM, "Lorenzo Isella" <lorenzo.ise...@gmail.com> wrote: > 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 >>> >>> >>> >>> ______________________________________________ >>> >>> 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. >>> >> >> >> >> -- >> Max ______________________________________________ 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.
