similar to: multi-class for BRT

 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

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

I applied bootpred for multinomial logistic reg. (with nnet package). I used same as theta.fit and theta.predict of R for my data. but give me error. Can I do this with response vriable;7 levels predictor variables:5 (1 classifier, 4 continuous)?   Thanks alot Azam   [[alternative HTML version deleted]]

Hi   I want to do leave-one-out cross-validation for multinomial logistic regression in R. I did multinomial logistic reg. by package nnet in R. How I do validation? by which function? response variable has 7 levels   please help me   Thanks alot Azam [[alternative HTML version deleted]]

Hi   I did validation for prediction by logistic regression according to following:   validationsize <- 23 set.seed(1) random<-runif(123) order(random) nrprofilesinsample<-sort(order(random)[1:100]) profilesample <- data[nrprofilesinsample,] profilevalidation <- data[-nrprofilesinsample,] salich<-profilesample$SALIC.H.1 salic.lr<-glm(salich~wetnessindex, profilesample,

Hi   I want to draw a plot from observed and predicted data and also shows threshold and data before threshold are identified with different color from data after threshold.   Suppose: abserved data are 0 or 1 predicted data= 0 to 1 threshold=0.5   Thanks alot     [[alternative HTML version deleted]]

Hello all   I have a geology map that has three level, bellow   <-geology lithology    landscape   landform     landform level is used as covariate (with codes=1,2,3,4,5) for training of neural network, but this level has missing data as NA. I want to replace the missing data of landform level with 0 (zero). Finally, landform will have codes  0,1,2,3,4,5.   please help me   Thanks alot.

I'm working across the statistical literature to find methods for calculating prediction intervals for GLM, BRT (boosted regression tree) models and MARS (multivariate adaptive regression spline) models, but unfortunately my statistical background is too weak to understand most of the stuff I read. I would by satisfied by knowing how to code this in R (and accept the methods as black

Dear All   I want to import the vector file (   .shp) to R. I could import the file by rgdal package before, by following:   geology<-readOGR('C:/geology//saga/geo.geom','finalgeology')   but now there is an error:   Error in ogrInfo(dsn = dsn, layer = layer, input_field_name_encoding = input_field_name_encoding) :           GDAL Error 4: .shx file is unreadable, or corrupt.

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)

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

You could break your 3 class problem into several (2 or 3) 2 class problems, and then use Andy's suggestion (see the CART book). There are several ways to break the problem into 2 class problems, and several ways to combine the resulting classifiers. Tom Dietterich, Jerry Friedman, Trevor Hastie and Rob Tibshirani, among others, have articles on the question, in places like Annals of

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 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

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

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

New versions of the caret (3.08) and caretLSF (1.12) packages have been released. caret (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for data splitting, pre-processing and model tuning, as well as other miscellaneous functions. In the new versions: - The elasticnet and the lasso (from the enet package)

New versions of the caret (3.08) and caretLSF (1.12) packages have been released. caret (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for data splitting, pre-processing and model tuning, as well as other miscellaneous functions. In the new versions: - The elasticnet and the lasso (from the enet package)

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 =