similar to: random forests for R

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

(Sorry for the cross-post--- I wasn't sure which list is more appropriate...) Hi everyone, I've run into segfaults when using my randomForest package on large dataset (e.g., 100 x 15200) and large number of trees (e.g., ntree=7000 and mtry=3000). I'm wondering if anyone can give me some hints on where to look for the problem. The randomForest package mainly consists of two things:

(Sorry for the cross-post--- I wasn't sure which list is more appropriate...) Hi everyone, I've run into segfaults when using my randomForest package on large dataset (e.g., 100 x 15200) and large number of trees (e.g., ntree=7000 and mtry=3000). I'm wondering if anyone can give me some hints on where to look for the problem. The randomForest package mainly consists of two things:

Dear mailinglist members, I was wondering if there was a way to re-evaluate the instances of a tree (in the forest) again after I have manually changed a splitpoint (or split variable) of a decision node. Here's an illustration: library("randomForest") forest.rf <- randomForest(formula = Species ~ ., data = iris, do.trace = TRUE, ntree = 3, mtry = 2, norm.votes = FALSE) # I am

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

A few days ago I asked whether there were options other than na.action=na.fail for the R port of Breiman?s randomForest; the function?s help page did not say anything about other options. I have since discovered that a pdf document called ?The randomForest Package? and made available by Andy Liaw (who made the tool available in R---thank you) does discuss an option. It is an implementation of

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, In R, how to do missing value replacement for test data in randome forest in the way Breiman decribed. thanks in advance iris

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

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,

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

Hello, When I plot or look at the error rate vector for a random forest (rf$err.rate) it looks like a descending function except for a few first points of the vector with error rates values lower(sometimes much lower) than the general level of error rates for a forest with such number of trees when the error rates stop descending. Does it mean that there is a tree(s) (that is built the first in

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

Dear R-devel, Last week I got several responses to my question about debugging segfaults in my code (original post below). After I changed the S_alloc() calls to Calloc()/Free(), the symptom was gone, but I was told to keep looking. So I did: o Switched to Calloc/Free. Electric Fence did not find any problem. o Put assert(index < bound); assert(index >=0); everywhere in the C routine

Hello, Does anybody know if the outscale option of randomForest yields the standarized version of the outlier measure for each case? or the results are only the raw values. Also I have notice that this measure presents very high variability. I mean if I repeat the experiment I am getting very different values for this measure and it is hard to flag the outliers. This does not happen with two other

Guys, I used Random Forest with a couple of data sets I had to predict for binary response. In all the cases, the AUC of the training set is coming to be 1. Is this always the case with random forests? Can someone please clarify this? I have given a simple example, first using logistic regression and then using random forests to explain the problem. AUC of the random forest is coming out to be

Hi all, I am trying to play around with the randomForest function for classification. I know its performance is great. I am currently using the default options. It has many options. How do I further tweak the options so that I can make its performance even better? What are the options that are mostly used? Thanks a lot! M [[alternative HTML version deleted]]

Dear all , I would like to use the function randomForest to predict the probability of relocation failure of a GPS collar as a function of several environmental variables x (both factor and numeric: slope, vegetation, etc.) on a given area. The response variable y is thus success (0)/failure(1) of the relocation, and the sampling unit is the pixel of a raster map. My aim is to build a map

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