Re: [R] caret package: custom summary function in trainControl doesn't work with oob?

Hi Max, Thanks for your help. In the case of randomForest, the keyword keep.inbag=TRUE in the train function provokes the return the information about which data rows were in-bag in each tree. That should provide the required info to re-compute the OOB error for any given alternative error definit

[R] caret package: custom summary function in trainControl doesn't work with oob?

Hi all, I've been using a custom summary function to optimise regression model methods using the caret package. This has worked smoothly. I've been using the default bootstrapping resampling method. For bagging models (specifically randomForest in this case) caret can, in theory, uses the out-of-b

Re: [R] Question about randomForest

r on the original data  since each tree was built using bootstrap > samples (about 70% of the original data), and the error rate of OOB is > likely higher than the prediction error of the original data as you > observed. > > Weidong > > On Sat, Nov 26, 2011 at 3:02 PM, Matthew Fra

[R] Question about randomForest

I've been using the R package randomForest but there is an aspect I cannot work out the meaning of. After calling the randomForest function, the returned object contains an element called prediction, which is the prediction obtained using all the trees (at least that's my understanding). I've check