How does Random Forest handle missing values?
Updated May 17, 2026
Short answer
Random Forest does not natively handle missing values; preprocessing is required.
Deep explanation
Most implementations require imputation before training. Some variants estimate missing values using surrogate splits or proximity.
Real-world example
Used in healthcare datasets where missing patient data is common.
Common mistakes
- Assuming Random Forest automatically handles NaNs.
Follow-up questions
- What is imputation?
- Which strategy is best?