similar to: Gradient Boosting Trees with correlated predictors in gbm

HI, Dear Greg, I AM A NEW to GBM package. Can boosting decision tree be implemented in 'gbm' package? Or 'gbm' can only be used for regression? IF can, DO I need to combine the rpart and gbm command? Thanks so much! -- Sincerely, Changbin -- [[alternative HTML version deleted]]

Hi R User, I was trying to find a final model in the following example by using the Boosted regression trees (GBM). The program gives the fitted values but I wanted to calculate the fitted value by hand to understand in depth. Would you give moe some hints on what is the final model for this example? Thanks KG ------- The following script I used #----------------------- library(dismo)

Hi I am using boosting for a classification and prediction problem. For some reason it is giving me an outcome that doesn't fall between 0 and 1 for the predictions. I have tried type="response" but it made no difference. Can anyone see what I am doing wrong? Screen output shown below: > boost.model <- gbm(as.factor(train$simNuance) ~ ., # formula +

Here is my code: mygbm<-gbm.fit(y=mytraindata[, 1], x=mytraindata[, -1], interaction.depth=4, shrinkage=0.001, n.trees=20000, bag.fraction=1, distribution="bernoulli") Here is the error: Error in gbm.fit(y = mytraindata[, 1], x = mytraindata[, -1], interaction.depth = 4, : The dataset size is too small or subsampling rate is too large: cRows*train.fraction*bag.fraction <=

I recently use gbm for a binary classification problem. As expected, it gets very good results, based on Area under ROC with 7-fold cross validation. However, the application (malware detection) is cost-sensitive, getting a FP (classify a clean sample as a dirty one) is much worse than getting a FN (miss a dirty sample). I would like to tune the gbm model biased to very low FP rate. For this

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 =

Hi, I am having problems passing arguments to method="gbm" using the train() function. I would like to train gbm using the laplace distribution or the quantile distribution. here is the code I used and the error: gbm.test <- train(x.enet, y.matrix[,7], method="gbm", distribution=list(name="quantile",alpha=0.5), verbose=FALSE,

Hi, there: I am wondering if I can find some detailed explanation on gbm or explanation on examples of gbm. thanks, Ed

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? --

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

Hi, I am trying to implement the Adaboost.M1. algorithm as described in "The Elements of Statistical Learning" p.301 I don't use Dtettling 's library "boost" because : - I don't understande the difference beetween Logitboost and L2boost - I 'd like to use larger trees than stumps. By using option weights set to (1/n, 1/n, ..., 1/n) in rpart or tree

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

Dear all, the help file for predict.gbm states that "The predictions from gbm do not include the offset term. The user may add the value of the offset to the predicted value if desired." I am just not sure how exactly, especially for a Poisson model, where I believe the offset is multiplicative ? For example: library(MASS) fit1 <- glm(Claims ~ District + Group + Age +

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 <- >

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

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

Hi I am using boosting for a classification and prediction problem. For some reason it is giving me an outcome that doesn't fall between 0 and 1 for the predictions. I have tried type="response" but it made no difference. Can anyone see what I am doing wrong? Screen output shown below: > boost.model <- gbm(as.factor(train$simNuance) ~ ., # formula +

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, 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

Dear List, The gbm package on Win 7 produces different results for the relative importance of input variables in R 32-bit relative to R 64-bit. Any idea why? Any idea which one is correct? Based on this example, it looks like the relative importance of 2 perfectly correlated predictors is "diluted" by half in 32-bit, whereas in 64-bit, one of these predictors gets all the importance