similar to: Error on random forest variable importance estimates

HI, Dear Andy, I run the RandomFOrest in R, and get the following resutls in variable importance: What is the meaning of MeanDecreaseAccuracy and MeanDecreaseGini? I found they are raw values, they are not scaled to 1, right? Which column if most similar to the variable rel.influence in Boosting? Thanks so much! > fit$importance 0 1

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

Hello, I am trying to explore the use of random forests for classification and am certain about the interpretation of the importance measurements. When having the option "importance = T" in the randomForest call, the resulting 'importance' element matrix has four columns with the following headings: 0 - mean raw importance score of variable x for class 0 (where

I am trying to use the random forests package for classification in R. The Variable Importance Measures listed are: -mean raw importance score of variable x for class 0 -mean raw importance score of variable x for class 1 -MeanDecreaseAccuracy -MeanDecreaseGini Now I know what these "mean" as in I know their definitions. What I want to know is how to use them. What I am trying to

Hi All, In the script below, the importance measure for column 4 (ie MeanDecreaseGini) indicated "Inf" for V7. Running the getTree command showed that "V7" had been selected at least twice in one of the trees for Random Forest. So the "Inf" command was not generated as a result of dividing the sum of the decreases by 0. Any suggestions on what may be causing the

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

Hi everyone, I have another "Random Forest" package question: - my (presumably incorrect) understanding of the varImpPlot is that it should plot the "% increase in MSE" and "IncNodePurity" exactly as can be found from the "importance" section of the model results. - However, the plot does not, in fact, match the "importance"

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

Full_Name: Daniel Normolle Version: 2.0.1 OS: Linux/Fedora Core 3 Submission from: (NULL) (141.214.17.5) varImpPlot in Random Forests 4.5-10 produces the error "incorrect number of subscripts on matrix" (and no plot) when applied to a randomForest object. This error did not occur with 4.5-4 or earlier versions.

Buenas, ¿Cómo calculariais el punto de inflexion de esta curva marcado en rojo? [X] He probado de la siguiente manera: 1- Haciendo la segunda derivada:No me sirve, porque m edetecta muchas veces esos casos 2- Mirando si el siguiente valor es mayor que el anterior. No me vale, ya que si hay ruido, no lo va a detectar. 3. Mirando que el punto siguiente crezca respecto al anterior. No me

I am trying to use the package RandomForest performing regression. The variable importance estimates are given as: "%IncMSE" and "IncNodePurity" Can anyone explain me what these refer to and how they are calculated? I found a lot of information on variable importance measures for classification problems, but nothing on regression. Thanks a lot. Mareike

Hi I am trying to explore the use of random forests for regression to identify the important environmental/microclimate variables involved in predicting the abundance of a species in different habitats, there are approx 40 variable and between 200 and 500 data points depending on the dataset. I have successfully used the randomForest package to conduct the analysis and looked at the %IncMSE

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 ).

Dear R-devel, Kurt had alerted me to the problem that the randomForest package that I maintain has been failing checks in R-devel. However, I just can't see why or where it's failing. I'd very much appreciate any pointer. The failure occur when running the example code in varImpPlot.Rd: > varImpPlot(mtcars.rf) Error in par(opar) : invalid value specified for graphical parameter

Hi, I have been using some code for multivariate random forests. The output from this code is a list object with all the same values as from randomForest, but the model object is, of course, not of the class randomForest. So, I was hoping to modify the code for predict.randomForest to work for predicting the multivariate model to new data. This is my first attempt at modifying code from a

Dear all, Version 4.3-0 of the randomForest package is now available on CRAN (in source; binaries will follow in due course). There are some interface changes and a few new features, as well as bug fixes. For those who had used previous versions, the important things to note are: 1. there's a namespace now, and 2. some functions have been renamed. The list of changes since 4.0-7 (last

Dear all, Version 4.3-0 of the randomForest package is now available on CRAN (in source; binaries will follow in due course). There are some interface changes and a few new features, as well as bug fixes. For those who had used previous versions, the important things to note are: 1. there's a namespace now, and 2. some functions have been renamed. The list of changes since 4.0-7 (last

Hi, I am having some trouble using R 2.13.1 for generating a pmml object of of class "c('randomForest.formula', 'randomForest')" I see that these methods are available: > methods(pmml) [1] pmml.coxph* pmml.hclust* pmml.itemsets* pmml.kmeans* pmml.ksvm* pmml.lm* pmml.multinom* pmml.nnet* pmml.rpart* [10] pmml.rsf* pmml.rules* pmml.survreg*

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

Hello! I want to estimate strength and correlation of RandomForest, but in package "randomForest" there is not an interface to get it. I think I must to change the source code. Is there any advise? Thanks, Li [[alternative HTML version deleted]]