How does Random Forest approximate Bayesian posterior predictive distributions?

RF approximates posterior predictive distributions through empirical averaging over randomized models.

In Bayesian inference, predictions integrate over parameter uncertainty. Random Forest approximates this by averaging predictions over randomized trees, which act as samples from an implicit prior over tree structures. Although not fully Bayesian, RF mimics posterior predictive averaging without explicit probabilistic modeling.