similar to: Random Forests theoretical question

"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.

Hi, Perhaps this is not the proper place to ask this question but I am out of options, therefore I apologize in advance. I want to know how the (upper bound?) generalization error of the random forest is determined using the out-of-bag estimate. I read in Breiman's paper that s and p determine the generalization error: p(1-s^2)/s^2. Does s stands for the strength of the individual tree or of

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 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 R users I want to combine multiply imputed data-sets generated from mice to do classfication. However, I have various questions regarding the use of mice library. For example suppose I want to predict the class in this data.frame: data(nhanes) mydf=nhanes mydf$class="pos" mydf$class[sample(1:nrow(mydf), size=0.5*nrow(mydf))]="neg" mydf$class=factor(mydf$class) First I

Sorry for the repost, but I've really been looking, and can't find any syntax direction on this issue... Just browsing the documentation, and searching the list came up short... I have some unbalanced data and was wondering if, in a "0" v "1" classification forest, some combo of these options might yield better predictions when the proportion of one class is low (less

Not sure where to even start on this.... I'm hoping there's some debugging I can do... I have a loop that cycles through several different data sets (same structure, different info), performing randomForest growth and predictions... saving out the predictions for later study... I get about 5 hours in (9%... of the planned iterations.. yikes!) and R just freezes. This happens in

Dear useRs, Release 1.0.0 of the new R package 'randomSurvivalForest' is now available on CRAN and its mirrors. The package implements Ishwaran and Kogalur's Random Survival Forests algorithm for right censored survival data. The algorithm is closely patterned after Breiman's random forests, but suitably modified for the survival setting. Some key features are: o An ensemble

Ok, I know R isn't an optimal environment for looping (or so I've heard) but I have a need to loop through columns of data and create new columns of data based on calculations within rows... I'm sure there's a help file, but I'm not sure what search terms to use to find it! The problem is that these new columns need to have names that I can later access... Like NewVar1,

Hi, I am doing some research on feature selection for classfication problem using genetic algorithm in a wrapper approach. I am wondering if there is some package which is already built for this purpose. I was advised before about dprep package but I don't think it used GA there (if I am wrong, please correct me!) Thanks, Ed

I've seen how to replace the NA's in a single column with a data frame *> mydata$ncigs[is.na(mydata$ncigs)]<-0 *But this is just one column... I have thousands of columns (!) that I need to do this, and I can't figure out a way, outside of the dreaded loop, do replace all NA's in an entire data frame (all vars) without naming each var separately. Yikes. I'm racking my

I have a very large data frame, and I'm doing a conversion of all columns into factors. Takes a while (thanks to folks here though, for making faster!), but am wondering about optimization from a memory perspective... Internally, am I better off assigning into a new data frame, or doing one of these: dataframe<-someoperation(dataframe) It would seem that re-assigning into the same data

Trying to get a myth backend running in fedora core 6 and xend 3.0.3 but having some issues with the DVB ATSC card, I have removed it from Dom0 and presented it to my vm, but I am unable to scan for channels...this is what lspci looks like for the card: lspci -v 00:00.0 Network controller: Techsan Electronics Co Ltd B2C2 FlexCopII DVB chip / Technisat SkyStar2 DVB card (rev 02) Subsystem:

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

You can test if the problem is accumulation in memory registers, which is certainly what this sounds like. Just do a loop over a reasonably small number of iterations and store or print the time between each iteration. If memory accumulation it will run optimally for the first few iterations, after which the time will increase noticeably (essentially exponentially, hence ultimately freezes up). If

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 I wanted to instal the exellent DVBviewer application for viewing DVB-T-Television under fedora9 / wine? Under WinXP i need to do the following 3 steps: 1) installing the Driver of my technisat DVB-hardware via the windows control-panel 2) installing the software of my technisat DVB-hardware 3) installing the DVBviewer application. So under fedora9 / wine i don't know to to the first step

On Sat, 2020-10-17 at 09:09 -0700, trix at redhat.com wrote: > From: Tom Rix <trix at redhat.com> > > This is a upcoming change to clean up a new warning treewide. > I am wondering if the change could be one mega patch (see below) or > normal patch per file about 100 patches or somewhere half way by collecting > early acks. > > clang has a number of useful, new