What is the role of permutation invariance in Random Forest ensembles?

Random Forest predictions are invariant to permutations of training samples due to exchangeability.

Because trees are trained on bootstrap samples, the order of training data does not affect the learned structure. This permutation invariance ensures that RF depends only on empirical distribution, not sequence ordering, making it stable under dataset shuffling.