Why do I keep reading about specifying training and test sets with random forest regression? As probably obvious from this question I am new to this method, but what I thought one of the cool things about it was that this is not needed because the out of bag sample functions as the test set, i.e. the method provides its own test set. Is this just needed under specific circumstances, e.g. for more methodological studies or am I missing some very fundamental aspect?? Thank you for any clarification.
You still need a separate test set (or outer round of cross-validation) if you're using those out-of-bag estimates to tune some aspect of the random forest, such as the number of features to consider for each split. If not, out-of-bag estimates can indeed substitute for a separate test set.
I may be able to give you a more specific answer if you provide the particular paper or papers you're talking about.