There are two methods to select subset of features during a tree construction in random forest:

According to Breiman, Leo in "Random Forests":

“… random forest with random features is formed by selecting at random, at each node, a small group of input variables to split on.”

Tin Kam Ho used the “random subspace method” where each tree got a random subset of features.

I can imagine that by selecting a subset of features at each node is more superior as the correlated variables can still be involved in the whole tree construction. Whereas if we select a subset of features for each tree, one of the correlated variables will lose its importance.

Are there any other reasons why one method can perform better than the other one?


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