similar to: RandomForest how to identify two classes when only one is present

I am sure this question has come up, but searching hasn't given me any results. So I need to enter this line: mx1 <- randomForest(X1 ~ elevation + slope + vegtype, data = moths.train) But the problem is that X1 is currently hard coded. I would instead like to be able to put in the value of X1 through a list. For example: list <- list("X1", "X2", "X3")

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 using the "caret" Package for predictors selection with a randomForest model. The following is the train function: rfFit<- train(x=trainRatios, y=trainClass, method="rf", importance = TRUE, do.trace = 100, keep.inbag = TRUE, tuneGrid = grid, trControl=bootControl, scale = TRUE, metric = "ROC") I wanted to use ROC as the metric for variable

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

Sir, This query is related to randomForest regression using R. I have a dataset called qsar.arff which I use as my training set and then I run the following function - rf=randomForest(x=train,y=trainy,xtest=train,ytest=trainy,ntree=500) where train is a matrix of predictors without the column to be predicted(the target column), trainy is the target column.I feed the same data

Hello, I came across a problem when building a randomForest model. Maybe someone can help me. I have a training- and a testdataset with a discrete response and ten predictors (numeric and factor variables). The two datasets are similar in terms of number of predictor, name of variables and datatype of variables (factor, numeric) except that only one predictor has got 20 levels in the training

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

Dear R-users, While making a prediction using the randomForest function (package randomForest) I'm getting the following error message: "Error in predict.randomForest(model, newdata = CV) : No forest component in the object" Here's my complete code. For reproducing this task, please find my 2 data sets attached ( http://www.nabble.com/file/p17855119/data.rar data.rar ).

Buenos días: Otra opción es escribir directamente el nombre de la función en la consola de R: > randomForest function (x, ...) UseMethod("randomForest") En este caso, la función randomForest() llama a UseMethod() para seleccionar el método adecuado. Podemos ver los métodos para randomForest con la función methods(): > methods(randomForest) [1] randomForest.default*

Hello I'd like to try out some functions in the package randomForest. Therefore, I did install this package. However, it is not possible to load the library, although I have R-Version 2.1.1 (i.e. later than 2.0.0). The commands I used and the Answers/Error from R is as follows: > install.packages("C://Programme//R//rw2011//library//randomForest_4.5-16.zip",

Hello, I've tried to install randomForest on a Ubuntu 8.04 Hardy Heron system. I've repeatedly rec'd the error: > install.packages("randomForest", dependencies = TRUE) ERROR: compiliation failed for package 'randomForest' ** Removing '/home/admuser/R/i486-pc-linux-gnu-library/2.6/randomForest' The downloaded packages are in

I used the following command to train a randomForest model train.rf <- randomForest(grp ~ ., data=tr, ntree=100, mtry=50) My question is how to save the trained model so that it can be loaded later for testing new samples? Thanks, Luk --------------------------------- [[alternative HTML version deleted]]

I am new to R and I'd like to use the randomForest package for my thesis (identifying important variables for more detailed analysis with other software). I have found extremely well written and helpful information on the usage of R. Unfortunately it seems to be very difficult to find similarly detailed tutorials for randomForest, and I just can't get it work with the information on

Just out of curiosity, I took the default "iris" example in the RF helpfile... but seeing the admonition against using the formula interface for large data sets, I wanted to play around a bit to see how the various options affected the output. Found something interesting I couldn't find documentation for... Just like the example... > set.seed(12) # to be sure I have

Hello, I am attempting to train a random forest model using the randomForest package on 500,000 rows and 8 columns (7 predictors, 1 response). The data set is the first block of data from the UCI Machine Learning Repo dataset "Record Linkage Comparison Patterns" with the slight modification that I dropped two columns with lots of NA's and I used knn imputation to fill in other gaps.

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

Hi there, We're trying to use randomForest to do some predictions. The test-harness for our code is pretty straightforward: library ('randomForest'); data202 <- read.csv ("random.csv", header=TRUE); x<- data202[1:50000,1:6]; y<- data202[1:50000,8]; y<- y[,drop=TRUE]; x2 <- data202[50001:60000,1:6]; y2 <- data202[50001:60000,8]; y2 <-

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))){

I have search the help archives and can't find a direct reference to the following issue: When installing randomForest on under CentOS 5.2 , R version 2.7.1 with gcc 4.1.2. We receive the following error (see below, can't find –lg2c) it is in the path! root@abcsci12 ~]# R CMD INSTALL /scisys/home/yanicrk/randomForest_4.5-28.tar.gz * Installing to library

Christian -- You don't provide enough information (like a call) to answer this. I suspect, though, that you may be subsetting in a way that passes randomForest no data. I'm not aware offhand of an easy way to get this error from randomForest. I tried creating some data superficially similar to yours to see whether something would break if there were only a single value in the variable