I am working with a small sample (n ~ 350) where I want to use a Random Forest approach to train a model to use on future new datapoints. I am using the randomForest package in R.
I understand the standard approach is to split the data into training and test data to validate the model. My concern lies with my small sample size. I also understand this concern could be assuaged using some type of k-fold validation.
However, why even perform the train-test split in the first place? Doesn't the Random Forest procedure bootstrap the data inherently? The randomForest package produces an MSE, which I understand is calculated using OOB predictions -- isn't this analogous to a train-test validation process? To me, it seems that this OOB MSE is analogous to a test MSE.
Sorry if I have a gross misunderstanding of the process here.