similar to: package gbm, predict.gbm with offset

Dear gbm users, When running predict.gbm() on a "large" dataset (150,000 rows, 300 columns, 500 trees), I notice that the memory used by R grows beyond reasonable limits. My 14GB of RAM are often not sufficient. I am interpreting this as a memory leak since there should be no reason to expand memory needs once the data are loaded and passed to predict.gbm() ? Running R version 2.9.2 on

Hello LIST, I'd like to use tune from e1071 to do a grid search for hyperparameter values in gbm. However, I can not get this to work. I note that there is no wrapper for gbm but that it is possible to use non-wrapped functions (like lm) without problem. Here's a snippet of code to illustrate. > data(mtcars) obj <- >

Dear List, I´m working on a classification problem. My response has 60 levels. I`m very interested in boosted trees like AdaBoost or gradient boosting machine as implemented in the package "gbm". Unfortunately gbm is only applicable for 2-class problems. Is anybody out there who can help me? Is there a way to use gbm() for multi-class problems? Maybe there is a way to transform my

Hello. I'm currently stuck with the same "what does interaction.depth really mean" stuff. Did you find out what the right answer is? Best regards, Boris Yangel. [[alternative HTML version deleted]]

I am using the gbm package for generalized boosted regression models, and would like to be able to extract the coefficients produced for storage in a database. I am already using R to automatically generate formulas that I can export to a database and store. For example, I have been using Dr. Harrell's lrm package to perform logistic regression, e.g.: output <-

HI, Dear Greg and R community, I have one question about the output of gbm package. the output of Boosting should be f(x), from it , how to calculate the probability for each observations in data set? SInce it is stochastic, how can guarantee that each observation in training data are selected at least once? IF SOME obs are not selected, how to calculate the training error? Thanks? --

I'm attempting to fit boosted regression trees to a censored response using IPCW weighting. I've implemented this through two libraries, mboost and gbm, which I believe should yield models that would perform comparably. This, however, is not the case - mboost performs much better. This seems odd. This issue is meaningful since the output of this regression needs to be implemented in a

On page 409 of "Applied Predictive Modeling" by Max Kuhn, it states that the gbm function can accomodate only two class problems when referring to the distribution parameter. >From gbm help re: the distribution parameter: Currently available options are "gaussian" (squared error), "laplace" (absolute loss), "tdist" (t-distribution

The crashes turned out to have nothing to do with the temp files. They seem to be caused by loading library(gbm). That is when many R sessions nearly simultaneously load the library R crashes with the message "The instruction at '0x63422398' referenced memory at '0x63422398'. The required data was not placed into memory because of an I/O error status of

Generalized Boosted Models (gbm) This package implements extensions to Y. Freund and R. Schapire's AdaBoost algorithm and J. Friedman's gradient boosting machine (aka multivariate adaptive regression trees, MART). It includes regression methods for least squares, absolute loss, logistic, Poisson, Cox proportional hazards/partial likelihood, and the AdaBoost exponential loss. It handles

Generalized Boosted Models (gbm) This package implements extensions to Y. Freund and R. Schapire's AdaBoost algorithm and J. Friedman's gradient boosting machine (aka multivariate adaptive regression trees, MART). It includes regression methods for least squares, absolute loss, logistic, Poisson, Cox proportional hazards/partial likelihood, and the AdaBoost exponential loss. It handles

Hi, there: I am always experiencing the scalability of some R packages. This time, I am trying gbm to do adaboosting on my project. Initially I tried to grow trees by using rpart on a dataset with 200 variables and 30,000 observations. Now, I am thinking if I can apply adaboosting on it. I am wondering if here is anyone who did a similar thing before and can provide some sample codes. Also any

Hi, Dear Greg, Sorry to bother you again. I have several questions about the 'gbm' package. if the train.fraction is less than 1 (ie. 0.5) , then the* first* 50% will be used to fit the model, the other 50% can be used to estimate the performance. if bag.fraction is 0.5, then gbm use the* random* 50% of the data to fit the model, and the other 50% data is used to estimate the

 Hello   I have questions about gbm package.  It seems we have to devide data to two part (training set and test set) for first.   1- trainig set for running of gbm function 2- test set for gbm.perf      is it rigth? I have 123 sample that I devided 100 for trainig and 23 for test.   So, parameter of cv.folds in gbm function is for what?   Thanks alot Azam       [[alternative HTML

Hello, I'm not sure if Im posting this on the right place, my apologies if not. I'm using the package gbm to generate boosted trees models, and was wondering if there is a simple way of getting a graphical output for a single tree of the sequence. I know the function "pretty.gbm.tree" can be used to print information for a single tree, but I've been unable to find a way to

I'm running R 2.11.1 on 64 bit Debian. I've had no problem installing any other CRAN packages, but installing package "gbm" fails due to: *** installing help indices ** building package indices ... ** testing if installed package can be loaded Error : .onAttach failed in attachNamespace() for 'gbm', details: call: NULL error: 'packageVersion' is not an

I extracting splitting rules from Greg Ridgeway's GBM 1.6-3.2 in R 2.15.2, so I can run classification in a production system outside of R. ?I have it working and verified for a dummy data set with all variable types (numeric, factor, ordered) and missing values, but in the titanic survivors data set the splitting rule for factors does not make sense. ?The attached code and log below explains

Hi, Does anyone know how to compile the gbm-1.5-1 package on a Sun sparc platform running Solaris 9? I have been using gcc-3.4.2 and getting an NAN undeclared error. Any help would be great. thanks, Jackson ___________________________________________________________________________ This email may contain confidential and/or privileged information for the sole use of the intended recipient.

Good afternoon Has anyone tried using Dr. Elith's BRT script? I cannot seem to run gbm.step from the installed gbm package. Is it something external to gbm? When I run the script itself <- gbm.step(data=model.data, gbm.x = colx:coly, gbm.y = colz, family = "bernoulli", tree.complexity = 5, learning.rate = 0.01, bag.fraction = 0.5) ... I

Hi, I have a geforce 4mx 440 agp 8x, and I'm trying to use the GBM library, (as jbarnes in: http://virtuousgeek.org/blog/index.php/jbarnes/2011/10/ and David Hermann in KMSCON: https://github.com/dvdhrm/kmscon), without success. when I try to create a gbm_device, I get: (below the code.) nouveau_drm_screen_create: unknown chipset nv18 dri_init_screen_helper: failed to create pipe_screen