What is the relationship between Random Forest and conditional quantile estimation?

Random Forest can estimate conditional quantiles using variations like quantile regression forests.

Standard Random Forest estimates conditional mean E[Y|X]. However, by tracking the distribution of training samples in leaf nodes across trees, one can approximate the conditional distribution P(Y|X). Quantile regression forests compute empirical quantiles from this distribution, enabling prediction intervals rather than point estimates.