similar to: Root forest/regional forest set up...

Good day, I wasn't sure whether this should go to the user list or the samba-technical list. I chose here based on the descriptions of the list. Forgive me if my understanding of the naming is inaccurate. It is my understanding that Samba3 (and I believe 4, as well) has a very powerful SID<->UID mapping mechanism which will auto create the UID in a range. This is what I mean by Unix ID

Can I configure Samba4 in such a way that I have two separate **forests** on a single machine? let?s say one for CompanyA and other for companyB? So essentially does Samba4 support multiple server instances like Samba3 as described here http://wiki.samba.org/index.php/Multiple_Server_Instances? If it does not yet, are there any known blockers in supporting this or it?s just a question of time

Hi everyone, I am facing a problem I really do not know how to resolve about detecting significant spatial structures in a region I am studying. The study is about the Miombo forest (wooded savanna). I am working in a 10 ha permanent forest plot, where all trees are mapped and identified. I sampled 24 more or less regulately scattered plots of 25 x 25 m over the 10 ha. In each plot of 25 x 25 m

Didn't realize the message was cc'ed to R-help. Here's my reply... ________________________________ From: Liaw, Andy Sent: Thursday, June 18, 2009 11:35 AM To: 'Li GUO' Subject: RE: Can I estimate strength and correlation of Random Forest in R package " randomForest"? The strength and correlation among trees in a random forest are based on the predictions of

Hi, I had two questions regarding random forests for regression. 1) I have read the original paper by Breiman as well as a paper dicussing an application of random forests and it appears that the one of the nice features of this technique is good predictive ability. However I have some data with which I have generated a linear model using lm(). I can get an RMS error of 0.43 and an R^2 of

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

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 ).

Hi, We have a test environment with Samba Version 4.18.5 which is set to Forest, Domain and lowest function Level of 2008 R2. I am trying to raise the forest and domain levels. However, when I try to raise the domain level, I get an error - "ERROR: Domain function level can't be higher than the lowest function level of a DC!" and when I try to raise the forest level I get an

Hi, Setting up a test environment for 4.19 rc2. However, can't we raise the lowest functional level higher than 2008 R2 in 4.18.x? Thanks & Regards, Anantha Raghava H A DISCLAIMER: This e-mail communication and any attachments may be privileged and confidential to Exzatech Consulting And Services Pvt. Ltd., Bangalore, and are intended only for the use of the recipients named above

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

Hi there, I am an environmental studies masters student trying to get my thesis out the door. I am also newbie at trees in general, but I like what I see in the literature about the random forest algorithm. I think I get the general gist of things, but even after reading stuff I?m unclear about how I could be getting the results I?m seeing. I obviously am missing something about how the split

On Fri, 6 Apr 2018 08:01:50 -0700 (MST) Lea Massiot via samba <samba at lists.samba.org> wrote: > Hello, > > My post is about having two Samba 4 AD DC at two different > geographical places and access resources bidirectionnaly through a > VPN as summarized in the schema below. > > ------------------------- > Geographical site 1 > ------------------------- > -

Hello R Gurus, I am perplexed by the different results I obtained when I ran code like this: set.seed(100) test1<-randomForest(BinaryY~., data=Xvars, trees=51, mtry=5, seed=200) predict(test1, newdata=cbind(NewBinaryY, NewXs), type="response") and this code: set.seed(100) test2<-randomForest(BinaryY~., data=Xvars, trees=51, mtry=5, seed=200, xtest=NewXs, ytest=NewBinarY) The

Hello, I am a phd student in Bioinformatics and I am using the Random Forest package in order to classify my data, but I have some questions. Is there a function in order to visualize the trees, so as to get the rules? Also, could you please provide me with the code of "randomForest" function, as I would like to see how it works. I was wondering if I can get the classification having

Hi, i'm using randomForest package and i have 2 questions: 1. Can i drop one tree from an RF object? 2. i have a 300 trees forest, but when i use the predict function on new data (with predict.all=TRUE) i get only 270 votes. did i do something wrong? Thanks -- View this message in context: http://n4.nabble.com/Random-Forest-tp1557464p1557464.html Sent from the R help mailing list archive at

Dear list, I performed a multivariate analysis on freshwater invertebrates data. So I obtained coordinates of my samples on the axes defining the first factorial plane (F1 and F2). I would like to see if the positions on my factorial plan could be linked to levels of impairment ('low' vs 'significant') for several water quality pressure categories and which pressure categories

Hi all (and Andy!), When running a randomForest run in R, I get the last part of an output (with do.trace=T) that looks like this: 1993 | 0.04606 130.43 | 1994 | 0.04605 130.40 | 1995 | 0.04605 130.43 | 1996 | 0.04605 130.43 | 1997 | 0.04606 130.44 | 1998 | 0.04607 130.47 | 1999 | 0.04606 130.46 | 2000 | 0.04605 130.42 | With the first column representing the

Hello, I am aware of the fact that the combine() function in the Random Forest package of R is meant to combine forests built from the same training set, but is there any way to combine trees built on different training sets? Both the training datasets used contain the same variables and classes, but their sizes are different. Thanks [[alternative HTML version deleted]]

now I am learining random forest and using random forest package, I can get the OOB error rates, and test set rate, now I want to get the training set error rate, how can I do? pgp.rf<-randomForest(x.tr,y.tr,x.ts,y.ts,ntree=1e3,keep.forest=FALSE,do.trace=1e2) using the code can get oob and test set error rate, if I replace x.ts and y.ts with x.tr and y.tr,respectively, is the error rate

*Philippe MALADJIAN Responsable informatique | administrateur système* Le 10/10/2018 à 08:30, Andrew Bartlett via samba a écrit : > On Fri, 2018-10-05 at 11:22 +0200, Philippe Maladjian via samba wrote: >> *Philippe MALADJIAN >> Responsable informatique | administrateur système* >> Ligne directe : +33 (0)4 72 14 50 66 | pmaladjian at hilaire.fr >>