How does Random Forest behave under missing-not-at-random (MNAR) data?

RF performs poorly under MNAR because missingness itself carries information not modeled explicitly.

In MNAR, missing values depend on unobserved variables or target itself. Standard imputation breaks this dependency, leading to biased splits. RF cannot inherently model missingness mechanisms, so predictions become systematically biased unless missingness is explicitly encoded.