similar to: adaboost more two classes

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 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 +

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

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

Hello, I performed a boosting analisis with adabag package to obtain a classification tree with the following set of commands: Tesis.boost <- adaboost.M1(Captura~., data=Tesis2, mfinal=2) > arb<-Tesis.boost$tree[[1]] > post(arb, file ="") > post(arb, file ="",title= "Arbol 1") I would like to know the meanning of the numbers that appeared in the

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

Dear R-helper, Is there possible to make this array: > a <- array(1:12, c(4, 3)) > a [,1] [,2] [,3] [1,] 1 5 9 [2,] 2 6 10 [3,] 3 7 11 [4,] 4 8 12 > like: c(1,5,9) c(2,6,10) c(3,7,11) c(4,8,12) Thank you very much in advance. Regards, Muhammad Subianto

I have a question about how one can modify or override the compilers that R uses for package installations? Or if perhaps this configuration is in some editable file somewhere. Initially I built the version of R 2.15.1 on Solaris SPARC (virtual T4), but found out the build was done as 32 bit. After some research, I found that the pre-compiled GCC version I had only allowed for 32 bit. I wanted

Dear all, I have a dataset train <- cbind(c(0,2,2,1,0), c(8,9,4,0,2), 6:10, c(-1, 1, 1, -1, 1)) test <- cbind(1:5, c(0,1,5,1,3), c(1,1,2,0,3) ,c(1, 1, -1, 1, 1)) I want to find which rows of train and test it different in its last column (column 4). The solution must be something like train [,1] [,2] [,3] [,4] [1,] 0 8 6 -1 [3,] 2 4 8 1 [4,] 1 0 9 -1

Thank you very much for your useful suggestions. These are exactly what I was looking for. foo <- list(foo1, foo2, foo3) lapply(foo, function(x) matrix(unlist(x), nrow = length(x), byrow = TRUE)) or lapply(foo, function(x) do.call('rbind', x)) Best, Muhammad Subianto On 4/11/06, Muhammad Subianto <msubianto at gmail.com> wrote: > Dear all, > I have a result my experiment

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 R-helper, I am new learning R. Now, I have a data set like: 24,2,3,3,1,1,2,3,0,1 45,1,3,10,1,1,3,4,0,1 43,2,3,7,1,1,3,4,0,1 42,3,2,9,1,1,3,3,0,1 36,3,3,8,1,1,3,2,0,1 19,4,4,0,1,1,3,3,0,1 38,2,3,6,1,1,3,2,0,1 21,3,3,1,1,0,3,2,0,1 27,2,3,3,1,1,3,4,0,1 45,1,1,8,1,1,2,2,1,1 ... with 3730 rows I want to remove comma (,) in data set. The result like: 24 2 3 3 1 1 2 3 0 1 45 1 3 10 1 1 3 4 0 1

I'm going to implement AdaBoost algorithm in R. Just wanted to ensure that there is no implementation of any boosting algorithm in R... don't want to reinvent the wheel... -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

I'm using Ubuntu 8.10 (Intrepid Ibex) and R 2.7.1. I've built a package from source (a modified version of gbm) and it contains some C++ code. I now want to cross-compile it to get a Windows version. I installed R using sudo apt-get update sudo apt-get install r-base sudo apt-get install r-base-dev So far as I can tell, I've also followed all the instructions in the guide

Dear all, I have a problem to make figures with two columns in package vcd. Here an example code I take from "\library\vcd\html\plot.loglm.html" What I need, I want to make two figures in one plot. How could I do that. I have tried with layout(rbind(c(1, 1, 2, 2))) but the same result, two plot. Best wishes, Muhammad Subianto library(vcd) oldpar <- par(mfrow=c(1, 2)) ## mosaic

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

Dear all, #Last week, I asked about merge x and y as list. #Now I have a dataset with list of list like: x <- list(list(matrix(1:20, 5, 4),matrix(1:20, 5, 4)), list(matrix(1:20, 5, 4),matrix(1:20, 5, 4))) y <- list(list(c(1, -1, -1, 1, 1),c(1, 1, -1, -1, -1)), list(c(1, 1, 1, 1, 1),c(1, 1, -1, 1, -1))) x y #I need merge x and y, I have tried with list.uni <-

Dear all, Suppose I have a dataset like below, then I take for example, 100 random sample "class" variable where contains "yes" and "no" respectively, 70% and 30%. I need a new 100 random sample from mydat dataset, but I can't get the result. Thanks you very much for any helps. Best, Muhammad Subianto mydat <- data.frame(size=c(30,12,15,10,12,12,25,30,20,14),

Hi, I have been searching everywhere for an implementation of those algorithms, but I have only observed them in Matlab and on the literature. I noticed a package called 'ada' in CRAN but it is not for multi class. I would be happy with just Adaboost.m2, Smoteboost over adaboost.m2 or any other combination that could account for imbalanced multiclass classification problems. Thanks!