similar to: problem to tunning RandomForest, an unexpected result

Dear Researches, I am using RF (in regression way) for analize several metrics extract from image. I am tuning RF setting a loop using different range of mtry, tree and nodesize using the lower value of MSE-OOB mtry from 1 to 5 nodesize from1 to 10 tree from 1 to 500 using this paper as refery Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O. (2007). Random Forest Models

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 ).

Apologies for any obtuseness in the following. We have been working on Version 2.0 of the randomSurvivalForest CRAN package and we're encountering a perplexing 'memory not mapped' segfault that we believe is "influenced" by GC. We essentially have two R functions, rsf.default(..), and predict.rsf(..) and two corresponding entry points, rsfGrow(...), and rsfPredict(...),

Dear All, I am using randomForest to predict the final selling price of some items. As it often happens, I have a lot of (noisy) historical data, but the question is not so much about data cleaning. The dataset for which I need to carry out some predictions are fairly recent sales or even some sales that will took place in the near future. As a consequence, historical data should be somehow

I am implementing an image classification algorithm using the randomForest package. The training data consists of 31000+ training cases over 26 variables, plus one factor predictor variable (the training class). The main issue I am encountering is very low overall classification accuracy (a lot of confusion between classes). However, I know from other classifications (including a regular decision

R programming question, not machine learning, although that's the content. Apologies to all for whom the following code is eye-burning. I am using foreach() to run a simulation on a randomForest model (actually conditional randomForest ... "party" package). The simulation is in two dimensions. examining how "mtry" and "ntrees" are related in terms of predictive

Hi, I would like to use a random Forest model to get an idea about which variables from a dataset may have some prognostic significance in a smallish study. The default for the number of trees seems to be 500. I tried changing the default to ntree=2000 or ntree=200 and the results appear identical. Have changed mtry from mtry=5 to mtry=6 successfully. Have seen same problem on both a Windows

Just curious if anyone else has got this error before, and if so, would know what I could do (if anything) to get past it: > mtry <- tuneRF(training, trainingdata$class, ntreeTry = 500, stepFactor = 2, improve = 0.05, trace = TRUE, plot = TRUE, doBest = FALSE) mtry = 13 OOB error = 0.62% Searching left ... mtry = 7 OOB error = 1.38% -1.222222 0.05 Searching right ... mtry = 26

Dear all, Does anybody can explain this: different results got when all the same parameters are used in the errorest() in library ipred, as the following? errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv", est.para=control.errorest(k=3), mtry=2)$err [1] 0.03333333 > errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv",

Dear R-helpers, I'm working on mass spectra in randomForest/R, and following the recommendations for the case of noisy variables, I don't want to use the default mtry (sqrt of nvariables), but I'm not sure up to which proportion mtry/nvariables it makes sense to increase mtry without "overtuning" RF. Let me tell my example: I have 106 spectra belonging to 4 classes, the

Thank you all for your help! So with all your suggestions we will try to run it on a computer with a 64 bits proccesor. But i've been told that the new R versions all work on a 32bits processor. I read in other posts that only the old R versions were capable of larger data sets and were running under 64 bit proccesors. I also read that they are adapting the new R version for 64 bits

Hello, I'm currently using MCRestimate package and I have a question regarding the MCRestimate function. Here is my code: NestedCV.rf<-MCRestimate(eset, "Class", classificatin.fun="RF.wrap", variableSel.fun="varSel.highest.var", poss.parameters= list(var.numbers=c(100), mtry=c(10,50), cross.outer=10,cross.inner=10,cross.repeat=3) I'm pretty sure that I

When mtry is equal to total number of features, you just get regular bagging (in the R package -- Breiman & Cutler's Fortran code samples variable with replacement, so you can't do bagging with that). There are cases when bagging will do better than random feature selection (i.e., RF), even in simulated data, but I'd say not very often. HTH, Andy From: Arne.Muller at

Dear R users, I've just released a new version of randomForest (available on CRAN now). This version contained quite a number of new features and bug fixes, compared to version prior to 4.0-x (and few more since 4.0-1). For those not familiar with randomForest, it's an ensemble classifier/regression tool. Please see http://www.math.usu.edu/~adele/forests/ for more detailed information,

Dear R users, I've just released a new version of randomForest (available on CRAN now). This version contained quite a number of new features and bug fixes, compared to version prior to 4.0-x (and few more since 4.0-1). For those not familiar with randomForest, it's an ensemble classifier/regression tool. Please see http://www.math.usu.edu/~adele/forests/ for more detailed information,

Hello, I am learning caret package, and I want to use the RFE to reduce the feature. I want to use RFE coupled Random Forest (RFE+FR) to complete this task. As we know, there are a number of pre-defined sets of functions, like random Forest(rfFuncs), however,I want to tune the parameters (mtr) when RFE, and then I write code below, but there is something wrong message, How can I deal with it?

I have been using random forest on a data set with 226 sites and 36 explanatory variables (continuous and categorical). When I use "tune.randomforest" to determine the best value to use in "mtry" there is a fairly consistent and steady decrease in MSE, with the optimum of "mtry" usually equal to 1. Why would that occur, and what does it signify? What I would

The `1' and `2' columns are the error rates within those classes. E.g., the last row of the `1' column should correspond to the class.error for "-", and the last row of the `2' column to the class.error for "+". (I would have thought that that should be fairly obvious, but I guess not. It mimics what Breiman and Cutler's Fortran code does.) I suspect

Hello, I'm trying to find out the optimal number of splits (mtry parameter) for a randomForest classification. The classification is binary and there are 32 explanatory variables (mostly factors with each up to 4 levels but also some numeric variables) and 575 cases. I've seen that although there are only 32 explanatory variables the best classification performance is reached when

Hi I listened to all your advise and ran my data on a computer with a 64 bits procesor but i still get the same error saying "it cannot allocate a vector of that size 1240 kb" . I don't want to cut my data in smaller pieces because we are looking at interaction. So are there any other options for me to try out or should i wait for the development of more advanced computers!