similar to: AdaBoost for R

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 all, I'm using adaBoost from the ada package to build a classification model. After training the model in R I'd like to use it in a Python application. Is it possible to export the model in some way to make translating into python easier? Any help would be greatly appreciated. Thanks. Bob

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!

Hello, does any one know how to interpret this output in R? > Classification with logitboost > fit <- logitboost(xlearn, ylearn, xtest, presel=50, mfinal=20) > summarize(fit, ytest) Minimal mcr: 0 achieved after 6 boosting step(s) Fixed mcr: 0 achieved after 20 boosting step(s) What is "mcr" mean? Thanks [[alternative HTML version deleted]]

I have some data in the Linux version of S-Plus, which I can not use anymore. The program is just broken and won't run. I'm trying to find a way to import that data to either Windows version of S-Plus (which I have running on my other machine) or R (Linux or Windows, it doesn't matter). Unfortunately, nothing seems to work. Windows S-Plus seem to ignore files from Linux .Data

Hello, I would like to know how to obtain the misclassification error when performing a boosting analisis with ADABAG package? With: > prop.table(Tesis.boostcv$confusion) I obtain the confusion matrix, but not the overall missclassification error. Thanks in advance, BSc. Cecilia Lezama Facultad de Ciencias - UDELAR Montevideo - Uruguay. [[alternative HTML version deleted]]

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 +

> From: Sirotkin, Alexander [mailto:demiurg at ti.com] > > P.S. Anybody who finds my postings insulting, please write a > mail filter > for my address. Although I can not understand why it may insult > anybody. > > P.P.S. Must be related to cultural differences, I guess... It's not insulting to me, personally, but quite possibly to the author of that

Everyone, I've got code in my package that uses LogitBoost from the caTools package. caTools does not have a namespace. My package also uses loads RWeka, which has a namespace, and also has a function called LogitBoost. After loading both packages, how can I be specific about running the version from caTools (since caTools:::LogitBoost won't work)? Thanks, Max

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

Hi, I am using dlda algorithm from supclust package and I am wondering if the output can be a continuous probability instead of discrete class label (zero or one) since it puts some restriction on convariance matrix, compared with lda, while the latter can. thanks, -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..."

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 R-Helper, I want to know, is there any function/package can handle adaboost more two classes? I know packages gbm and boost, but there are only for 2 classes (correct me if I mistake). Regards, Muhammad Subianto

Hi all; Happy new year. I have got the following error rror in if (nrow(object$splits) > 0) { : argument is of length zero when I am running the following codes. train <- c(sample(1:27,18), sample(28:54, 18), sample(55:81, 8)) a2011.adaboost <- boosting(median_kod ~ ., data = b[train, ], boos=TRUE, mfinal = 10, control = rpart.control(minsplit = 0)) Regards, Greg [[alternative

Hi, all, When running > AB.fit=adaboost(ylearn, xlearn, xtest, presel=0) I got the following error: Error in `[[<-.data.frame`(`*tmp*`, preds, value = c(4L, 6L, 6L, 6L, 3L, : replacement has 186 rows, data has 62 The data structure is attached below: [1] "ylearn" [1] 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 [40] 1 1 1 1 1 1 1 0

Ok, so basically I have a dataframe named data_frame data_frame contains: startdate startprice endpricethreshold1 endpricethreshold2 endpricethreshold3 all of these endpricethresholds are true/false binary vectors. They are true or false depending on whether the endprice was above or below whatever the endpricethreshold is. now I want to try to use lets say the general linear model to have

Hi All, I am trying to use the C50 package to build classification trees in R. Unfortunately there is not enought documentation around its use. Can anyone explain to me - how to prune the decision trees? Regards, Indrajit [[alternative HTML version deleted]]

When I sink the output of an RWeka model to a text file, the output file appears empty: library(RWeka) model = LogitBoost(Species~.,data=iris) print(model) sink("output.txt") print(model) #file output.txt is created, but it is blank sink() Am I doing anything wrong? [[alternative HTML version deleted]]

Hi, I am using some classifiers in RWeka packages and met a couple problems. (1) J48 implements C45 classifier, the C45 should be able to handle missing values in both training set and test set. But I found the J48 classifier can not be evaluated on test set with missing values--it just ignore them. (2) The ensemble classifiers in RWeka such as bagging and boosting: there is a

dear list, i have a problem using the adaboost function from the package boost. running the example of leukemia works out very well, but so it does not on my own data. i always get the following 22 warnings: 1: cannot compute exact p-value with ties in: wilcox.test.default(x[which(y == 0)], x[which(y == 1)]) can anybody please help me with the meaning of this warning. thnak you stefan