search for: ntrees

...ugh that's the content. Apologies to all for whom the following code is eye-burning. I am using foreach() to run a simulation on a randomForest model (actually conditional randomForest ... "party" package). The simulation is in two dimensions. examining how "mtry" and "ntrees" are related in terms of predictive accuracy in ten-fold cross validation. My problem is one of functional programming. The "loops" for simulation are functionalised so they can be passed to foreach and bundled of to my 4 cores. However, I'm making a mess of passing in arguments...

Buenas Tengo en un string guardado lo siguiente: > parametros [1] "ntree=10" "ntree=30" "ntree=50" "ntree=100" "ntree=200" Con un bucle for quiero ir metiendolo en el modelo, pero no se muy bien como hacerlo, ya que con deparse no me funciona, con get tampoco (obvio, no es un objeto), y no se muy bien como hacerlo de manera dinamica

Hi, I would like to use a random Forest model to get an idea about which variables from a dataset may have some prognostic significance in a smallish study. The default for the number of trees seems to be 500. I tried changing the default to ntree=2000 or ntree=200 and the results appear identical. Have changed mtry from mtry=5 to mtry=6 successfully. Have seen same problem on both a Windows

Hi Sacha, On second thought, perhaps this is more the direction that you want ... X2 = cbind(X_train,y_train) colnames(X2)[3] = "y" regr2<-randomForest(y~x1+x2, data=X2,maxnodes=10, ntree=10) regr regr2 #Make prediction predictions= predict(regr, X_test) predictions2= predict(regr2, X_test) HTH, Eric On Tue, May 9, 2023 at 6:40?AM Eric Berger <ericjberger at gmail.com>

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

...s.  Si alguien se le ocurre algo se lo agradezco. df  <- read.table(file="xxxx.csv", header=T, sep=";", dec=",") df  <-na.omit(df) dfgrupo<-subset(df,df$parametroslaboratorio=="Aflatoxinas ByG") niveles <- as.numeric(dfgrupo$param) ntrees <- max(niveles)  xrange <- range(as.Date(dfgrupo$fecha)) yrange <- range(dfgrupo$z) plot(xrange, yrange, type = "n", xlab = "Fecha del interlaboratorio",       ylab = "Z-score") colors <- rainbow(ntrees) ltipo <- c(1:ntrees) char &lt...

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: tree...

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%

...7 11 7 10 4 6 13 8 7 10 9 7 6 8 9 7 11 8 8 9 > 10 5 8 11 12 6 5 10 10 6 10 10 5 10 13 9 13 10 10 6 12 > [485] 8 7 9 12 10 9 7 7 14 6 9 6 6 8 10 6 >> vtmp <- treesize(iris.rf) >> sum(vtmp) > > Por defecto al no especificar nada, el "ntrees" de randomForest() es 500. > Efectivamente generas 500 árboles como ves en el número de elementos que > devuelve "treesize(iris.rf)". > > Y cada árbol, tiene el número de nodos que ves en el valor de cada uno de > los elementos que igualmente devuelve "treesize(ir...

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

...5 8 11 12 6 5 10 10 6 10 10 5 10 13 9 13 10 10 6 12 >>> [485] 8 7 9 12 10 9 7 7 14 6 9 6 6 8 10 6 >>> >>>> vtmp <- treesize(iris.rf) >>>> sum(vtmp) >>>> >>> >>> Por defecto al no especificar nada, el "ntrees" de randomForest() es 500. >>> Efectivamente generas 500 árboles como ves en el número de elementos que >>> devuelve "treesize(iris.rf)". >>> >>> Y cada árbol, tiene el número de nodos que ves en el valor de cada uno de >>> los elementos q...

Dear R-experts, Here below a toy example with some error messages, especially at the end of the code (Tuning the parameters). Your help to correct my R code would be highly appreciated. ####################################### #libraries library(lattice) library(ggplot2) library(caret) library(randomForest) ?? #Data

Dear List, I built a package under both Mac and Win 7 (both on R 2.12.0) . One of the functions in the package is set up to print a status message using the code below: if (verbose) if ((i %% 10) == 0 && i < ntree) message( "", i, "out of", ntree, "trees so far...") This works perfectly on the Mac. However, on Win 7 the message is not printed

Hi all, Can some one tell me the difference between the following two formulas? 1. epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) 2.epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) [[alternative HTML version deleted]]

Dear All, I am using randomForest to predict the final selling price of some items. As it often happens, I have a lot of (noisy) historical data, but the question is not so much about data cleaning. The dataset for which I need to carry out some predictions are fairly recent sales or even some sales that will took place in the near future. As a consequence, historical data should be somehow

...small number like 10, it will run for hours. I use the (x,y) rather than the (formula,data). My code: > sdata<-read.csv("D://zSignal Dump//XXXX//XXXX.csv") > sdata1<-subset(sdata,select=-38) > sdata2<-subset(sdata,select=38) > res<-randomForest(x=sdata1,y=sdata2,ntrees=10) Am I doing anything wrong? Or do you have other suggestions? Are there any other packages to do the same thing? I will appreciate if anyone can help me out, thanks! Thanks and Best regards, ------------------------------------------------ Jia, Zou (×Þ¼Î), Ph.D. IBM Research -- China Diamond...

Dear Researches, I am using RF (in regression way) for analize several metrics extract from image. I am tuning RF setting a loop using different range of mtry, tree and nodesize using the lower value of MSE-OOB mtry from 1 to 5 nodesize from1 to 10 tree from 1 to 500 using this paper as refery Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O. (2007). Random Forest Models

Hi, is it correct that i need ~ 2GB RAM that it's possible to work with the default setting ntree=500 and a data.frame with 100.000 rows and max. 10 columns for training and testing? P.S. It's possible calculate approximate the memory demand for different settings with RF? Many thanks & regards, Christian

Greg, Two thoughts: 1. It might be possible that 'vars' is a reserved word of sorts and if you change the name of your vector RF might be happier 2. A way that works for me is to call importance as follows: sel.imp <- importance(sel.rf, class=NULL, scale=TRUE, type=NULL) and then use the 'names' of the imp data frame to be absolutely clear to RF you are talking about the

...uoting Carlos Ortega <cof en qualityexcellence.es>: > Hola, > > A "treesize()" le tienes que pasar como parámetro el objeto randomForest de > tu modelo. > Y obtiene el número de nodos de cada uno de los árboles que hayas indicado > en el valor del parámetros "ntrees" de "randomForest". Por defecto "ntrees" > tiene un valor de 500. > Mira qué valor tiene "ntrees" en tu modelo "randomForest", que seguramente > le hayas indicado un valor de 1000... > > Saludos, > Carlos Ortega > www.qualityexcellen...