similar to: eliminating constant variables

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 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,

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, 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 +

I color some area grey with polygon() (with a red border) and then I want to have the dashed red border in the legend as well. How do I manage it? And I want to mix (latex) expressions with text in my legend. Just execute my lines below and you know want I mean. Or pass by at http://de.wikipedia.org/wiki/Bild:GBM.png to see the picture online. Thomas bm <- function(n=500, from=0, to=1) {

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]]

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

Full_Name: The Manager Version: 2.0.1 OS: Solaris 9 Submission from: (NULL) (129.67.80.243) > install.packages("gbm") trying URL `http://cran.uk.r-project.org/src/contrib/PACKAGES' Content type `text/plain; charset=ISO-8859-1' length 52975 bytes opened URL ================================================== downloaded 51Kb trying URL

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

Hola de nuevo. Se me olvidaba la principal razón para utilizar gbm.step del paquete dismo. Como sabéis, los boosted si sobreajustan (a diferencia de los random forest o cualquier otro bootstrap) pero gbm.step hace validación cruzada para determinar el nº óptimo de árboles y evitarlo. Es fundamental. La opción que me queda, Carlos, es hacerlo con gbm, pero muchas veces, y usar el

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 =

Gracias Carlos, mi idea es construir un cono, un cilindro u otros cuerpos geométrico y luego graficarlos. Alguna idea de como empezar? Muchas gracias como siempre El lun., 19 de feb. de 2018 15:06, <r-help-es-request en r-project.org> escribió: > Envíe los mensajes para la lista R-help-es a > r-help-es en r-project.org > > Para subscribirse o anular su subscripción a

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

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)

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

 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

Gracias Carlos. Hasta donde yo entiendo si las hay: El argumento family puede ser: "gaussian" (for minimizing squared error); por lo que tiene que ser numérica "bernoulli" (logistic regression for 0-1 out-comes); binaria por narices "poisson" (count outcomes; requires the response to be a positive integer); numérica también, pues. La única podría ser

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 +

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

Dear R expert I have come across the GBM package for R and it seemed appropriate for my research. I am trying to predict the number of FPGA resources required by a Software Function if it were mapped onto hardware. As input I use software metrics (a lot of them). I already use several regression techniques, and the graphs I produce with GBM look promising. Now my question... I see that the