What is the role of asymptotic independence in Random Forest ensembles?

As the number of trees grows, predictions become weakly dependent, approximating independence in the limit.

Although trees are not strictly independent due to shared training data, increasing randomness in bootstrap sampling and feature selection reduces dependency. In the limit of infinite trees, dependence between any finite subset becomes negligible for variance computation, enabling law of large numbers to apply effectively.