It is common to represent categorical features with one-hot encoding, but this approach is suboptimal for tree learners. Particularly for high-cardinality categorical features, a tree built on one-hot features tends to be unbalanced and needs to grow very deep to achieve good accuracy.
Instead of one-hot encoding, the optimal solution is to split on a categorical feature by partitioning its categories into 2 subsets.
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