similar to: help with RandomForest classwt option

"classwt" in the current version of the randomForest package doesn't work too well. (It's what was in version 3.x of the original Fortran code by Breiman and Cutler, not the one in the new Fortran code.) I'd advise against using it. "sampsize" and "strata" can be use in conjunction. If "strata" is not specified, the class labels will be used.

Sorry for the repost, but I've really been looking, and can't find any syntax direction on this issue... Just browsing the documentation, and searching the list came up short... I have some unbalanced data and was wondering if, in a "0" v "1" classification forest, some combo of these options might yield better predictions when the proportion of one class is low (less

Hi All, I am looking for a reference that explains how the randomForest function in the randomForest package uses the classwt parameter. Here: http://tolstoy.newcastle.edu.au/R/e4/help/08/05/12088.html Andy Liaw suggests not using classwt. And according to: http://r.789695.n4.nabble.com/R-help-with-RandomForest-classwt-option-td817149.html it has "not been implemented" as of 2007.

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

Hi Andy, I know this topic has been discussed before on the R-help, but I was wondering if you could offer some advice specific to my application. I'm using the R random forest package to compare two classes of data, the number of cases in each class relatively low, 28 in class 1 and 9 in class 2. I'd really like to use R environment to analyze this data, however I'm finding it

Hi, here are three results of random forest (version 4.0-1). The results seem to be more or less the same which is strange because I changed the classwt. I hoped that for example classwt=c(0.45,0.1,0.45) would result in fewer cases classified as class 2. Did I understand something wrong? Christian x1rf <- randomForest(x=as.data.frame(mfilters[cvtrain,]),

Just browsing the documentation, and searching the list came up short... I have some unbalance data and was wondering if, in a "0" v "1" classification forest, if these options might yield better predictions when the proportion of one class is low (less than 10% in a sample of 2,000 observations). Not sure how to specify these terms... from the docs, we have: classwt: Priors

Could not find it in ?randomForest. Thank you for your help! -- Dimitri Liakhovitski Ninah.com Dimitri.Liakhovitski at ninah.com

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

Hi group, I am trying to do a RF with approx 250,000 cases. My objective is to determine the risk factors of a person being readmitted to hospital (response=1) or else (response=0). Only 10%, or 25,000 cases were readmitted. I've heard about down-sampling and class weight approach and am wondering if R can do it. Even some reference to articles will help. >From the statistical point

Hello, I'm trying to find out the optimal number of splits (mtry parameter) for a randomForest classification. The classification is binary and there are 32 explanatory variables (mostly factors with each up to 4 levels but also some numeric variables) and 575 cases. I've seen that although there are only 32 explanatory variables the best classification performance is reached when

I am new to R and I'd like to use the randomForest package for my thesis (identifying important variables for more detailed analysis with other software). I have found extremely well written and helpful information on the usage of R. Unfortunately it seems to be very difficult to find similarly detailed tutorials for randomForest, and I just can't get it work with the information on

According to Dr. Breiman, the RF should be more accurate method than a single tree. However, the performance of each method seems to depend on the proprotion of outcome variable in my case. My data set is a typical classification problem (predict bad guys). When I ran both of them with different proportion of outcome variables(there's a criterion to measure the degree of bad behavior), I

Hi! I am dealing with random forest using R. Is there a way to sample a fixed no.of rows from a dataset for use with different trees in random Forest. To be more clear, my data set contains 1500 rows, and I am growing 500 trees in Random Forest Is it possible to sample only 500 rows of data from the data set and use it for different trees in the forest. I mean each tree of the forest should use

> From: Weiwei Shi > > it works. > thanks, > > but: (just curious) > why i tried previously and i got > > > is.vector(sample.size) > [1] TRUE Because a list is also a vector: > a <- c(list(1), list(2)) > a [[1]] [1] 1 [[2]] [1] 2 > is.vector(a) [1] TRUE > is.numeric(a) [1] FALSE Actually, the way I initialize a list of known length is by

Hello all, I have become somewhat confused with options available for dealing with a highly unbalanced data set (10000 in one class, 50 in the other). As a summary I am unsure: a) if I am perform the two class weighting methods properly, b) if the data are too unbalanced and that this type of analysis is appropriate and c) if there is any interaction between the weighting for class imbalances

Dear R-help, This is about randomForest's handling of NA and NaNs in test set data. Currently, if the test set data contains an NA or NaN then predict.randomForest will skip that row in the output. I would like to change that behavior to outputting an NA. Can this be done with flags to randomForest? If not can some sort of wrapper be built to put the NAs back in? thanks, Clayton

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]]

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

Hello, supposing that I have two or three clear categories for my data, lets say pet preferece across fish, cat, dog. Lets say most people rate their preference as being mostly one of the categories. I want to do pca on the data to see three 'groups' of people, one group for fish, one for cat and one for dog. I would like to see the odd person who likes both or all three in the