similar to: OOB packets and port forwarding

Hi all, I've been using a custom summary function to optimise regression model methods using the caret package. This has worked smoothly. I've been using the default bootstrapping resampling method. For bagging models (specifically randomForest in this case) caret can, in theory, uses the out-of-bag (oob) error estimate from the model instead of resampling, which (in theory) is largely

Gracias Carlos. No uso caret, pero lo miraré. Quoting Carlos Ortega <cof en qualityexcellence.es>: > Hola, > > Creo que si utilizas "caret" y en la función "trainControl()" defines "oob" > como criterio de randomización, puedes luego recuperar del objeto del > modelo, las predicciones individuales... > > Saludos, > Carlos Ortega >

Hi, I have puppet 3.0.1 installed with passenger and the clients are unables to request a certificate. I get a httpd error. 10.1.1.9 - - [11/Dec/2012:16:09:44 +0000] "GET /production/certificate/puppet-client2.tcl-oob.net? HTTP/1.1" 500 11298 "-" "-" When I run puppet master without passenger, I am able to request and sign certificates. I see this in http

Muy buenas, ¿sabe alguien cómo obtener las predicciones sobre el out of bag que hace randomForest? Manuel . -- Dr Manuel Mendoza Department of Biogeography and Global Change National Museum of Natural History (MNCN) Spanish Scientific Council (CSIC) C/ Serrano 115bis, 28006 MADRID Spain

When syncing one backup system (running dirvish, but that's not really relevant) to a second long-term backup system which uses btrfs snapshots, I get the following output: rsync: link "/parasite.oob.telegraaf.net/vservers-var-www/tree/elo/var/www/lokaalx/openx-htdocs/etc/xmlcache/.svn/prop-base/cache_etc-changes_schema-tables-core-12934.bin.svn-base" (in backup-aquagirl) =>

Are there any known problems converting dtmf from oob over iax2 to inband over rtp/ulaw? Obviously it works when converting to inband over pri/ulaw et al, but how about rtp? I've got packet traces that confirm that 2833 packets are properly generated when I have 2833 configured for the rtp link, but the other side seems to be ignoring those packets. So I tried inband on that link; nothing

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

Hello! I think I am relatively clear on how predictor importance (the first one) is calculated by Random Forests for a Classification tree: Importance of predictor P1 when the response variable is categorical: 1. For out-of-bag (oob) cases, randomly permute their values on predictor P1 and then put them down the tree 2. For a given tree, subtract the number of votes for the correct class in the

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I tried rfImpute (randomForest) and as far as I understood should it replace NA`s using a proximity matrix: > set.seed(100000) > Subset5Imputed<-rfImpute(Sex~., data=Subset5) ntree OOB 1 2 300: 11.78% 12.36% 11.21% ntree OOB 1 2 300: 12.07% 12.64%

Hi all, Had struggled in getting "Strata" in randomForest to work on this. Can I get randomForest for each of its TREE, to get ALL sample from some strata to build tree, while leaving some strata TOTALLY untouched as oob? e.g. in below, how I can tell RF to, - for tree 1 in the forest, to use only Site A and B to build the tree, while using the WHOLE Site C data for the oob error

What seems to be the problem? I think you forgot to include a description of the issues you're seeing. On Sep 18, 2017 11:23 PM, <ygrishin-lists at mail2.ca> wrote: Hello looks like the device is not supported, the logs are as follows: --- # dmesg | grep nouve [ 2.789899] fb: switching to nouveaufb from VESA VGA [ 2.790060] nouveau 0000:01:00.0: NVIDIA GT218 (0a8d00b1) [

Just out of curiosity, I took the default "iris" example in the RF helpfile... but seeing the admonition against using the formula interface for large data sets, I wanted to play around a bit to see how the various options affected the output. Found something interesting I couldn't find documentation for... Just like the example... > set.seed(12) # to be sure I have

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

https://bugzilla.netfilter.org/show_bug.cgi?id=876 Summary: bizarre handling of "related" connection packets (wrong OUTPUT interface assigned) Product: netfilter/iptables Version: unspecified Platform: x86_64 OS/Version: Fedora Status: NEW Severity: normal Priority: P5

Generically, yes, the hardware is supported. On Mon, Sep 18, 2017 at 11:42 PM, <ygrishin-lists at mail2.ca> wrote: > I will update the thread as soon as I get the hardware back. It was > something serious like artifacts or no video in X so that I had to resort to > proprietary drivers. > So the question is whether this hardware is supported or not. > > > On 2017-09-18

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

I am having trouble with the rfImpute function in the randomForest package. Here is a sample... clunk.roughfix<-na.roughfix(clunk) > > clunk.impute<-rfImpute(CONVERT~.,data=clunk) ntree OOB 1 2 300: 26.80% 3.83% 85.37% ntree OOB 1 2 300: 18.56% 5.74% 51.22% Error in randomForest.default(xf, y, ntree = ntree, ..., do.trace = ntree, : NA not

I've built two RF objects (RF1 and RF2) and have tried to combine them, but I get the following error: Error in rf$votes + ifelse(is.na(rflist[[i]]$votes), 0, rflist[[i]]$votes) : non-conformable arrays In addition: Warning message: In rf$oob.times + rflist[[i]]$oob.times : longer object length is not a multiple of shorter object length Both RF models use the same variables, although

Hi all, I recently learned that it is possible for `as.data.frame.matrix()` to produce a data frame with 0 columns that is also entirely missing a `names` attribute, and I think this is a bug: ``` # No `names`, weird! attributes(as.data.frame(matrix(nrow = 0, ncol = 0))) #> $class #> [1] "data.frame" #> #> $row.names #> integer(0) # This is what I expected

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