I am training a random forest on a text data set (that I represent with synthetic features) and I am willing to assess the quality of the features I am creating.
So far, I focused on the out-of-bag MSE. Alternatively, I could cross-validate the random forest. Though it seems more rigorous to use cross-validation, it will be (much) slower.
Is using the out-of-bag MSE a wrong approach for this task ? Are there any known biases ?