I have been reading some stackoverflow questions on how to handle nominal features for decision tree (sklearn implementation).
One of the answer states that :
Using a OneHotEncoder is the only current valid way, allowing arbitrary splits not dependent on the label ordering, but is computationally expensive.
But if we use LabelEncoder and make our tree go deep enough it will eventually isolate each category and the output remains the same.
So whats is the advantage of OneHotEncoding then?