What is the role of ergodicity in Random Forest training dynamics?

Random Forest relies on ergodicity assumptions to ensure ensemble averages approximate expected predictions.

Ergodicity implies that time (or ensemble) averages converge to expectation over a distribution. In Random Forest, averaging over many randomly generated trees approximates expectation over the stochastic tree-generating process. This assumption underpins convergence of ensemble predictions to stable limits as number of trees increases.