similar to: Random Forest Variable Importance

Dear R users, I have a question on the confusion matrix generated by function randomForest. I used the entire data set to generate the forest, for example: > print(iris.rf) Call: randomForest(formula = Species ~ ., data = iris, importance = TRUE, keep.forest = TRUE) confusion setosa versicolor virginica class.error setosa 50 0 0 0.00

Hi, I need help with the randomForest prediction. i run the folowing code: > iris.rf <- randomForest(Species ~ ., data=iris, > importance=TRUE,keep.forest=TRUE, proximity=TRUE) > pr<-predict(iris.rf,iris,predict.all=T) > iris.rf$votes[53,] setosa versicolor virginica 0.0000000 0.8074866 0.1925134 > table(pr$individual[53,])/500 versicolor virginica 0.928

Guys, I used Random Forest with a couple of data sets I had to predict for binary response. In all the cases, the AUC of the training set is coming to be 1. Is this always the case with random forests? Can someone please clarify this? I have given a simple example, first using logistic regression and then using random forests to explain the problem. AUC of the random forest is coming out to be

Hi all, I have a dataset where each point is assigned to a class A, B, C, or D. Each point is also assigned to a study site. Each study site is coded with a number ranging between 1-100. This information is stored in the vector studySites. I want to run randomForests using stratified sampling, so I chose the option strata = factor(studySites) But I am not sure how to control the number of

Hello, I am using the R randomForest package to classify variable stars. I have a training set of 1755 stars described by (too) many variables. Some of these variables are highly correlated. I believe that I understand how randomForest works and how the variable importance are evaluated (through variable permutations). Here are my questions. 1) variable importance error? Is there any ways

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

Hi, everyone, I am a newbie on R. Now I want to do image pixel classification by random forest. But I has not a clear understanding on random forest. Here is some question: As for an image, for example its size is 512x512 and has only one variable -- gray level. The histogram of the image looks like mixture Gaussian Model, say Gauss distribution (u1,sigma1), (u2,sigma2),(u3,sigma3). And a

I have a question on the output generated by randomForest in classification mode, specifically, the confusion matrix. The confusion matrix lists the various classes and how the forest classified each one, plus the classification error. Are these numbers essentially averages over all the trees in the forest? If so, is there a way I can get the standard deviation values out of the randomForest,

The verbose option gives a display like: > rf.500 <- + randomForest(new.x,trn.y,do.trace=20,ntree=100,nodesize=500, + importance=T) | Out-of-bag | Tree | MSE %Var(y) | 20 | 0.9279 100.84 | What is the meaning of %var(y)>100%? I expected that to correspond to a model that was worse than random, but the predictions seem much better than that on

i am new to R and using the random forest package. is there a way to specify the x-axis scale range for the variable importance plot? many thanks. -alison -- View this message in context: http://www.nabble.com/specifying-x-axis-scale-on-random-forest-variable-importance-plot-tp19780560p19780560.html Sent from the R help mailing list archive at Nabble.com.

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 everybody, I used random forest regression to explain the patterns of species richness and a bunch of climate variables (e.g. Temperature, precipitation, etc.) All are continuos variables. My results are really interesting and my model explained 96,7% of the variance. Now I am trying to take advantage of the importance variable function and depicts the observed patterns using partial

Didn't realize the message was cc'ed to R-help. Here's my reply... ________________________________ From: Liaw, Andy Sent: Thursday, June 18, 2009 11:35 AM To: 'Li GUO' Subject: RE: Can I estimate strength and correlation of Random Forest in R package " randomForest"? The strength and correlation among trees in a random forest are based on the predictions of

Friends, I have noticed that many publications that use RF report variable importance as a function of mean decrease in accuracy rather than mean decrease in gini. Am I correct that the mean decrease in accuracy is just the mean decrease in gini divided by 100? Thanks, Helen Mills Poulos Yale School of Forestry

Hi, I'm using the package randomForest to generate a classifier for the exemplary iris data set: data(iris) iris.rf<-randomForest(Species~.,iris) Is it possible to print all decision trees in the generated forest? If so, can the trees be also written to disk? What I actually need is to translate the decision trees in a random forest into equivalent C++ if-then-else constructs to

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

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, I'm experimenting with random forests and want to perform a binary classification task. I've tried some of the sample codes in the help files and things run, but I get a message to the effect 'you don't have very many unique values in the target - are you sure you want to do regression?' (sorry, don't know exact message but r is busy now so can't check). In

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

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