similar to: factor predictor using random forest

Hi, I'm using package "caret" to rank predictors using random forest model and draw predictors importance plot. I used below commands: rf.fit<-randomForest(x,y,ntree=500,importance=TRUE) ## "x" is matrix whose columns are predictors, "y" is a binary resonse vector ## Then I got the ranked predictors by ranking

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

Hi, Can someone please offer me some guidance? I imported some data. One of the columns called "JOBTITLE" when imported was imported as a factor column with 416 levels. I subset the data in such a way that only 4 levels have data in "JOBTITLE" and tried running randomForest but it complained about "JOBTITLE" having more than 32 categories. I know that is the limit

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 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]]

I have a question about predictor importances in randomForest. Once I've run randomForest and got my object, I get their importances: rfresult$importance I also get the "standard errors" of the permutation-based importance measure: rfresult$importanceSD I have 2 questions: 1. Because I am dealing with regressions, I am getting an importance object (rfresult$importance) with two

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

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, I'm very new to R. I am most interested in the variable importance measures that result from randomForest, but many of my predictors are highly correlated. My first question is: 1. do highly correlated variables render variable importance measures in randomForest invalid? and 2. I know that cforest is robust to highly correlated variables, however, I do not have enough space on my

> ---------- Forwarded message ---------- > Date: Mon, 18 Jul 2011 10:17:00 -0700 (PDT) > From: KHOFF <kuphoff at gmail.com> > To: r-help at r-project.org > Subject: [R] cforest - keep.forest = false option? > > Hi, > > I'm very new to R. I am most interested in the variable importance > measures > that result from randomForest, but many of my predictors

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

Hola, Me he iniciado hace poco en Machine Learning, y tengo una duda sobre mis conjuntos de datos: el primero tiene 37 variables explicativas y 116 instancias, y el segundo, 140 variables explicativas y 195 instancias. El primero lo veo bien, ya que hay 3 veces más casos que variables explicativas, pero creo que el segundo caso puede suponer un problema al haber casi el mismo número de

Hi, I am trying to print out my confusion matrix after having created my random forest. I have put in this command: fit<-randomForest(MMS_ENABLED_HANDSET~.,data=dat,ntree=500,mtry=14, na.action=na.omit,confusion=TRUE) but I can't get it to give me the confusion matrix, anyone know how this works? Thansk! Ruben [[alternative HTML version deleted]]

After checking the original data in Excel for blanks and running Summary(cm3) to identify any null values in my data, I'm unable to identify an instances. Yet when I attempted to use the data in Random Forest, I get the following error. Is there something that Random Forest is reading as null which is not actually null? Is there a better way to check for this? > library(randomForest) >

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

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

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>

Hi, I have a data set with 1000 observations and 260 predictors. The predictor variables are all ordinal. There are 2 classes labeled as, F and T with class proportions of 0.44 and 0.56, respectively. In a call to the function randomForest() with mytry=1 and nodesize=1 and ntree=100 the resulting classifier puts all observations in class T. When I change nodesize to nodesize=5 I get the

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

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