How does Random Forest behave in non-IID data distributions?

Random Forest performance degrades when data is not independently and identically distributed.

RF assumes IID sampling for bootstrap validity. In non-IID scenarios (temporal, spatial, grouped data), bootstrap samples violate independence assumptions, leading to biased tree structures and unreliable OOB estimates. Specialized variants like grouped RF or time-aware sampling are needed.