What is class imbalance handling in Random Forest?
Updated May 17, 2026
Short answer
Class imbalance can be handled using class weights or resampling.
Deep explanation
Random Forest can assign higher penalties to minority classes or use balanced bootstrapping to improve detection.
Real-world example
Fraud detection systems rely heavily on imbalance handling.
Common mistakes
- Ignoring imbalance leading to biased models.
Follow-up questions
- What is SMOTE?
- Is accuracy reliable in imbalance?