That’s a good question, since the earlier random decision forests by Tin Kam Ho used the “random subspace method,” where each tree got a random subset of features.

“Our method relies on an autonomous, pseudo-random procedure to select a small number of dimensions from a given feature space …”

  • Ho, Tin Kam. “The random subspace method for constructing decision forests.” IEEE transactions on pattern analysis and machine intelligence 20.8 (1998): 832-844.

However, a few years later, Leo Breiman described the procedure of selecting different subsets of features for each node (while a tree was given the full set of features) — Leo Breiman’s formulation has become the “trademark” random forest algorithm that we typically refer to these days when we speak of “random forest”

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

  • Breiman, Leo. “Random Forests” Machine learning 45.1 (2001): 5-32.

To answer your question: Each tree gets the full set of features, but at each node, only a random subset of features is considered.



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