similar to: Can't seem to finish a randomForest.... Just goes and goes!

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

At 12:01 03/04/04 +0200, you wrote: >Content-Transfer-Encoding: 8bit >From: solares at unsl.edu.ar >Precedence: list >MIME-Version: 1.0 >Cc: >To: R-help at stat.math.ethz.ch >Date: Fri, 2 Apr 2004 12:47:48 -0300 (ART) >Message-ID: <50155.209.13.250.66.1080920868.squirrel at inter17.unsl.edu.ar> >Content-Type: text/plain;charset=iso-8859-1 >Subject: [R] convert

"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.

Several ways: 1. Read ?randomForest, especially the `Value' section. 2. Look at str(myforest.rf). 3. Look at print.randomForest. If the forest has 100 trees, then the mse and rsq are vectors with 100 elements each, the i-th element being the mse (or rsq) of the forest consisting of the first i trees. So the last element is the mse (or rsq) of the whole forest. HTH, Andy > From: David

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

I would like to build a forest of regression trees to see how well some covariates predict a response variable and to examine the importance of the covariates. I have a small number of covariates (8) and large number of records (27368). The response and all of the covariates are continuous variables. A cursory examination of the covariates does not suggest they are correlated in a simple fashion

Hello there, I am working on an extremely unbalanced two class classification problems. I wanna use "classwt" with "down sampling" together. By checking the rfNews() in R, it looks that classwt is not working yet. Then I looked at the software from Salford. I did not find the down sampling option. I am wondering if you have any experience to deal with this problem. Do you

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

Hi, dears, I am processing some data with 60 columns, and 286,730 rows. Most columns are numerical value, and some columns are categorical value. It turns out that: when ntree sets to the default value (500), it says "can not allocate a vector of 1.1 GB size"; And when I set ntree to be a very small number like 10, it will run for hours. I use the (x,y) rather than the (formula,data).

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

Buenas tardes a todos. El paquete randomForest tiene la función treesize, que es el nº de nodos. Me dan valores realmente elevados (en torno a 1000), y eso me parece extraño. ¿sabéis si es así? Gracias, 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

Gracias Carlos y Javier, ntrees es el nº de árboles y treesize sus respectivos tamaños (nº de nodos) ntree: Number of trees to grow. This should not be set to too small ...... treesize: Size of trees (number of nodes) in and ensemble. Puse 1000 árboles (ntree=1000), si, pero la función treesize te da el nº de nodos: treesize(RFfit, terminal=TRUE) me da un vector de 1000 elementos (uno

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

I'm trying to apply a function defined in the VW R docs, that attemps to convert a data.table object to Vowpal Wabbit format. In the process i'm getting the error in printf mentioned in the subject. The original function is here: https://github.com/JohnLangford/vowpal_wabbit/blob/master/R/dt2vw.R Below there is a small example that reproduces the error. The function works great with

Buenas. El Paquete pdp es muy fácil de usar, pero cuando se lo aplico a mis datos me da: Error in eval(stats::getCall(object)$data) : object 'x.data' not found. Os copio abajo un ejemplo de aplicación a un RF. El mio es de un boosted regression trees (paquete gbm). No sé si esa puede ser la razón del error. En el paquete pdp no especifica que sea solo para RF, aunque en los

Wouldn't it make sense to have this in the man page? The 8192-byte limitation for 'fmt' is mentioned but not this one. Thanks, H. On 08/25/2015 02:08 AM, Prof Brian Ripley wrote: > From the sources: > > #define MAXNARGS 100 > /* ^^^ not entirely arbitrary, but strongly linked to > allowing %$1 to %$99 !*/ > > > > On 22/08/2015 04:21, Martin

Si, Carlos. Yo hago lo mismo, pero esos mismos numeritos salen enormes. > treesize(RFfit) [1] 4304 4302 4311 4319 4343 4298 4298 4311 4349 4327 4331 4317 4294 4321 4283 4362 [17] 4300 4330 4266 4331 4308 4352 4294 4315 4372 4349 4331 4347 4329 4348 4298 4335 [33] 4346 4396 4345 4313 4293 4276 4353 4272 4304 4325 4317 4336 4308 4351 4374 4324 [49] 4386 4359 4311 4346 4300

Muchas gracias Carlos, como siempre. Es raro que se me pasase. En su momento miré todos los argumentos del RF, como hago siempre, pero ese lo había olvidado. La verdad es que funcionaba estupendamente, pero me parecía extraño. Aunque dado que los RF no sobreajustan, no hay problema con que sus árboles sean todo lo grandes que quieras. Lo he testado con una base de datos externa y explica

I am new to R and I'd like to use the randomForest package for my thesis (identifying important variables for more detailed analysis with other software). I have found extremely well written and helpful information on the usage of R. Unfortunately it seems to be very difficult to find similarly detailed tutorials for randomForest, and I just can't get it work with the information on

Hola: No se muy bien si es esto lo que preguntas, pero el código de todos los scripts está en el fichero: https://cran.r-project.org/src/contrib/randomForest_4.7-1.1.tar.gz Saludos. On Sun, 19 Mar 2023 04:35:44 +0100 Manuel Mendoza <mmendoza en fulbrightmail.org> wrote: > Buenos días, ¿cómo podría ver el código con el que el paquete randomForest > hace el random forest? >