I'm a bit confused as to why gridsearchCV is not needed with random forests. I
understand that with RF, each tree will only get to see a partial
representation of the data.
However, if I wanted to tune some parameters of the RF, wouldn't I still need
to do gridsearch? If that is the case, does sklearn use the out of bag error to
find the best classifier, or the score method?
Thank you,
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