Dear Rene,

It is a nice question. Do you have any related paper on this?

Dear Rene,

It is a nice question. Do you have any related paper on this?

If you are asking for more general articles on random forests...yes. E.g.:

Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological methods, 14(4), 323.

Also there is quite some literature from the package authors themselves (e.g. http://topepo.github.io/caret/index.html). It is possible that the answer to my question is hidden somewhere in these articles but I did not find it yet.

Cheers,

René

Hi

I usually avoid "oob" because it can only be used with tree-based models, so it is not really useful if you want to compare different models.

I use repeated cross validation or bootstrap cross validation (i.e. "repeatedcv" or "boot"). In both cases the model is estimated on the training set/ inner bootstrap sample and the classification error (Kappa & Accuracy) on the test set/ outer bootstrap.

Stefano

Dear Stefano,

thanks for your attempt to answer my question. I guess you misunderstood. I am absolutely interested in a case where we use random forests (tree based). In random forests out-of-bag samples (oob) are an integral part. That´s why I was asking what would happen if I replace "oob" with another resampling method.