similar to: Running randomForests on large datasets

Hi, I am using randomForests for a classification problem. The predict function in the randomForest library, when asked to return the probabilities, has precision of two digits after the decimal. I need at least four digits of precision for the predicted probabilities. How do I achieve this? Thank you, Nagu

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

Dear all, I am trying to train a randomForest using all my control data (12,000 cases, ~ 20 explanatory variables, 2 classes). Because of memory constraints, I have split my data into 7 subsets and trained a randomForest for each, hoping that using combine() afterwards would solve the memory issue. Unfortunately, combine() still runs out of memory. Is there anything else I can do? (I am not using

I am attempting to use randomForests to look for interesting genes in microarray data with 216genes, 2 classes and 52 samples. My data.frame is 52x217 with the last column, V217 being the class(1 or 2). When I try lung.rf <- randomForest(V217 ~ ., data=tlSA216cda, importance= TRUE, proximity = TRUE) the GUI crashes. I am running R-1.6.2 under windo$e98, and most

Hello everybody, I'm testing the randomForest package in order to do some simulations and I get some trouble with the prediction of new values. The random forest computation is fine but each time I try to predict values with the newly created object, I get an error message. I thought I was because NA values in the dataframe, but I cleaned them and still got the same error. What am I

Hi all, I am trying to play around with the randomForest function for classification. I know its performance is great. I am currently using the default options. It has many options. How do I further tweak the options so that I can make its performance even better? What are the options that are mostly used? Thanks a lot! M [[alternative HTML version deleted]]

I try to use ?randomForest to find variables that are the most important to divide my dataset (continuous, categorical variables) in two given groups. But when I plot the outliers: plot(outlier(FemMalSex_NAavoid88.rf33, cls=FemMalSex_NAavoid88$Sex), type="h",col=c("red","green")[as.numeric(FemMalSex_NAavoid88$Sex)]) it seems to me that all my values appear as

Hi, I am trying to model a dataset with the response variable Y, which has 6 levels { Great, Greater, Greatest, Weak, Weaker, Weakest}, and predictor variables X, with continuous and factor variables using random forests in R. The variable Y acts like an ordinal variable, but I recoded it as factor variable. I ran a simulation and got OOB estimate of error rate 60%. I validated against some

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, I get the following error when I try to predict the probabilities of a test sample: Error in predict.randomForest(fit.EBA.OM.rf.50, x.OM, type = "prob") : New factor levels not present in the training data I have about 630 predictor variables in the dataset x.OM (25 factor variables and the remaining are continuous variables). Any ideas on how to trace it? Thank you, Nagu

Hi all, When running a randomForest run using the following command: forestplas=randomForest(Prev~.,data=plas,ntree=200000) print(forestplas) I get the following result: Call: randomForest(formula = Prev ~ ., data = plas, ntree = 2e+05, importance = TRUE) Type of random forest: regression Number of trees: 2e+05 No. of variables tried at each split: 5

Hello, When I plot or look at the error rate vector for a random forest (rf$err.rate) it looks like a descending function except for a few first points of the vector with error rates values lower(sometimes much lower) than the general level of error rates for a forest with such number of trees when the error rates stop descending. Does it mean that there is a tree(s) (that is built the first in

Has anyone sucessfully used the maxnodes feature in randomForest? I tried setting it, but when it is non-NULL I always get back a forest in which all trees have size 1. I am using a continuous response (regression). Any help would be appreciated. Thanks. [[alternative HTML version deleted]]

A few days ago I asked whether there were options other than na.action=na.fail for the R port of Breiman?s randomForest; the function?s help page did not say anything about other options. I have since discovered that a pdf document called ?The randomForest Package? and made available by Andy Liaw (who made the tool available in R---thank you) does discuss an option. It is an implementation of

Hi I listened to all your advise and ran my data on a computer with a 64 bits procesor but i still get the same error saying "it cannot allocate a vector of that size 1240 kb" . I don't want to cut my data in smaller pieces because we are looking at interaction. So are there any other options for me to try out or should i wait for the development of more advanced computers!

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

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).

Hello, I am attempting to train a random forest model using the randomForest package on 500,000 rows and 8 columns (7 predictors, 1 response). The data set is the first block of data from the UCI Machine Learning Repo dataset "Record Linkage Comparison Patterns" with the slight modification that I dropped two columns with lots of NA's and I used knn imputation to fill in other gaps.

I am implementing an image classification algorithm using the randomForest package. The training data consists of 31000+ training cases over 26 variables, plus one factor predictor variable (the training class). The main issue I am encountering is very low overall classification accuracy (a lot of confusion between classes). However, I know from other classifications (including a regular decision

You need to give us more details, like how you call randomForest, versions of the package and R itself, etc. Also, see if this helps you: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/32918.html Andy From: Eleni Rapsomaniki > > Dear all, > > I am trying to train a randomForest using all my control data > (12,000 cases, ~ 20 explanatory variables, 2 classes). > Because