similar to: new version of randomForest

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,

The link from http://cran.r-project.org/src/contrib/PACKAGES.html#randomForest seems to be broken. To get the file try http://cran.r-project.org/src/contrib/randomForest_3.3-4.tar.gz instead. > -----Original Message----- > From: Liaw, Andy [mailto:andy_liaw at merck.com] > Sent: Thursday, September 26, 2002 8:08 PM > To: 'r-announce at stat.math.ethz.ch' > Subject:

Dear all, Version 4.3-0 of the randomForest package is now available on CRAN (in source; binaries will follow in due course). There are some interface changes and a few new features, as well as bug fixes. For those who had used previous versions, the important things to note are: 1. there's a namespace now, and 2. some functions have been renamed. The list of changes since 4.0-7 (last

Dear all, Version 4.3-0 of the randomForest package is now available on CRAN (in source; binaries will follow in due course). There are some interface changes and a few new features, as well as bug fixes. For those who had used previous versions, the important things to note are: 1. there's a namespace now, and 2. some functions have been renamed. The list of changes since 4.0-7 (last

We are organizing three projects to honor Leo Breiman this year. First, Salford Systems' Data Mining Conference will open with a special session devoted to his scientific contributions. Second, we would like to construct Leo's scientific "family tree," identifying Leo's students, collaborators, and others whose work has been influenced in an important way by Leo's

Hi, I am having some trouble using R 2.13.1 for generating a pmml object of of class "c('randomForest.formula', 'randomForest')" I see that these methods are available: > methods(pmml) [1] pmml.coxph* pmml.hclust* pmml.itemsets* pmml.kmeans* pmml.ksvm* pmml.lm* pmml.multinom* pmml.nnet* pmml.rpart* [10] pmml.rsf* pmml.rules* pmml.survreg*

> From: Weiwei Shi > > it works. > thanks, > > but: (just curious) > why i tried previously and i got > > > is.vector(sample.size) > [1] TRUE Because a list is also a vector: > a <- c(list(1), list(2)) > a [[1]] [1] 1 [[2]] [1] 2 > is.vector(a) [1] TRUE > is.numeric(a) [1] FALSE Actually, the way I initialize a list of known length is by

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

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

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

Dear R-devel, A user reported a strange problem with predict.randomForest in the randomForest package yesterday, and I'm baffled by it. The code at the end of the message produces the error. The problem is that, in predict.randomForest, there's a .Fortran call to "runforest". One of the arguments passed in is "countts", which is a vector of doubles. The error

"classwt" in the current version of the randomForest package doesn't work too well. (It's what was in version 3.x of the original Fortran code by Breiman and Cutler, not the one in the new Fortran code.) I'd advise against using it. "sampsize" and "strata" can be use in conjunction. If "strata" is not specified, the class labels will be used.

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

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

Dear R-devel, I'm encountering a strange problem in a Rd file that I'm working on. In the "Value" section, I have something like: ===================== \value{ An object of class \code{randomForest}, which is a list with the following components: \item{call}{the original call to \code{randomForest}} ... For classification problem, the following are also included:

Hello R-devel: I have written a package, called "woods", that does classification trees (R function CT), and currently, only the most basic functionality of Random Forest, e.g. bagged trees with choices about sample size, with/without replacement, size of (random) subset of covariates drawn when nodes are split. My reason for writing this is twofold. First, I wanted to base this

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

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