How does Random Forest behave under structured missingness patterns?

Structured missingness can bias splits because missing patterns encode information.

When missing values follow structured patterns (e.g., conditional on class or time), RF may inadvertently use missingness as a predictive signal if encoded improperly. Standard imputation breaks structure, while surrogate splits or missing indicators can partially preserve information.