similar to: randomForest - classifier switch

hello, I want to use randomForest to classify a matrix which is 331030?42,the last column is class signal.I use ? Memebers.rf<-randomForest(class~.,data=Memebers,proximity=TRUE,mtry=6,ntree=200) which told me" the error is matrix(0,n,n) set too elements" then I use: Memebers.rf<-randomForest(class~.,data=Memebers,importance=TRUE,proximity=TRUE) which told me"the error is

Hello, I am struggling to produce an MDS plot using the randomForest package with a moderately large data set. My data set has one categorical response variables, 7 predictor variables and just under 19000 observations. That means my proximity matrix is approximately 133000 by 133000 which is quite large. To train a random forest on this large a dataset I have to use my institutions high

I getting "Error in matrix(0, n, n) : too many elements specified" while building randomForest model, which looks like memory allocation error. Software versions are: randomForest 4.5-25, R version 2.7.1 Dataset is big (~90K rows, ~200 columns), but this is on a big machine ( ~120G RAM) and I call randomForest like this: randomForest(x,y) i.e. in supervised mode and not requesting

Greetings R Users! I am posting to inquire about the proximity matrix in the randomForest R-package. I am having difficulty pushing very large data through the algorithm and it appears to hang on the building of the prox matrix. I have read on Dr. Breiman's website that in the original code a choice can be made between using an N x N matrix OR to increase the ability to compute large

Hi Sacha, On second thought, perhaps this is more the direction that you want ... X2 = cbind(X_train,y_train) colnames(X2)[3] = "y" regr2<-randomForest(y~x1+x2, data=X2,maxnodes=10, ntree=10) regr regr2 #Make prediction predictions= predict(regr, X_test) predictions2= predict(regr2, X_test) HTH, Eric On Tue, May 9, 2023 at 6:40?AM Eric Berger <ericjberger at gmail.com>

Dear all, I am trying to calculate the proximity matrix for a data set with 16 variables and 6804 observations using random forests. I have a Pentium 4, 3.00GHz processor with 1 GB of RAM. When I use the command randomForest(data.scale,proximity=T) I get the warning message Error: cannot allocate vector of size 361675 kb Is this because I have reached the limit of what my computer is

I am attempting to use randomForests to look for interesting genes in microarray data with 216genes, 2 classes and 52 samples. My data.frame is 52x217 with the last column, V217 being the class(1 or 2). When I try lung.rf <- randomForest(V217 ~ ., data=tlSA216cda, importance= TRUE, proximity = TRUE) the GUI crashes. I am running R-1.6.2 under windo$e98, and most

Dear R-users, I have a problem with randomForest{outlier}. After running the following code ( that produces a silly data set and builds a model with randomForest ): ####################### library(randomForest) set.seed(0) ## build data set X <- rbind( matrix( runif(n=400,min=-1,max=1), ncol = 10 ) , rep(1,times= 10 ) ) Y <- matrix( nrow = nrow(X), ncol = 1) for( i in (1:nrow(X))){

Hi I am new to R, randomForest and I have read about how to use it in your old mails. I have also run the predictions examples from CRAN. But I still don't understand how to use it right. The thing that I don't understand is how to run the result from the randomForest on one line (post) with newdata to get a good guess. What I mean is if I put in a new observation of iris how do I

Dear all, Can anybody give me some hint on the following error msg I got with using randomForest? I have two-class classification problem. The data file "sample" is: ---------------------------------------------------------- udomain.edu udomain.hcs hpclass 1 1.0000 1 not 2 NA 2 not 3 NA 0.8 not 4 NA 0.2 hp 5 NA 0.9 hp ------------------------------------------------------------ The

Hi, I'm experimenting with random forests and want to perform a binary classification task. I've tried some of the sample codes in the help files and things run, but I get a message to the effect 'you don't have very many unique values in the target - are you sure you want to do regression?' (sorry, don't know exact message but r is busy now so can't check). In

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When you have fairly large data, _do not use the formula interface_, as a couple of copies of the data would be made. Try simply: Myforest.rf <- randomForest(Mydata[, -46], Mydata[,46], ntrees=100, mtry=7) [Note that you don't need to set proximity (not proximities) or importance to FALSE, as that's the default already.] You might also want to use

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I try to find the most important variables to divide my dataset as given in a categorical variable. code: Test.rf4<-randomForest(Sex~.,na.action=na.roughfix, data=Subset4, importance=TRUE, proximity=TRUE, ntree=10000, do.trace=1000, keep.forest=FALSE) My dataset contains also ordered

Hi This is a question regarding classification performance using different methods. So far I've tried NaiveBayes (klaR package), svm (e1071) package and randomForest (randomForest). What has puzzled me is that randomForest seems to perform far better (32% classification error) than svm and NaiveBayes, which have similar classification errors (45%, 48% respectively). A similar difference in

Dear R-help, what are the limits on xtest? > NOT_A.rf <- randomForest (log10(Y[!A] ) ~ . , data = notA_desc , proximity=T ,xtest = A_desc) *** caught segfault *** address 0x9cdd000, cause 'memory not mapped' Segmentation fault I don't think that the matrix are large: notA_desc is 651 obs of 27 variables A_desc is 17 obs of 27 variables thanks in advance, Clayton

Hi all, Can some one tell me the difference between the following two formulas? 1. epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) 2.epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) [[alternative HTML version deleted]]

Greetings. I'm trying to determine whether to use rpart or randomForest for a classification tree. Has anybody tested efficacy formally? I've run both and the confusion matrix for rf beats rpart. I've looking at the rf help page and am unable to figure out how to extract the tree. But more than that I'm looking for a more comprehensive user's guide for randomForest including

Dear all, I am trying to train a randomForest using all my control data (12,000 cases, ~ 20 explanatory variables, 2 classes). Because of memory constraints, I have split my data into 7 subsets and trained a randomForest for each, hoping that using combine() afterwards would solve the memory issue. Unfortunately, combine() still runs out of memory. Is there anything else I can do? (I am not using

I'm having trouble calling randomForest::partialPlot programmatically. It tries to use name of the (R) variable as the data column name. Example: library(randomForest) iris.rf <- randomForest(Species ~ ., data=iris, importance=TRUE, proximity=TRUE) partialPlot(iris.rf, iris, Sepal.Width) # works partialPlot(iris.rf, iris, "Sepal.Width") # works (function(var.name)