juniorEnsemble Learning
What is a Random Forest and how does it work?
Updated May 16, 2026
Short answer
Random Forest is an ensemble of decision trees trained on random subsets of data and features.
Deep explanation
Random Forest uses bagging to create multiple decision trees trained on bootstrapped datasets. Additionally, it introduces feature randomness by selecting a subset of features at each split. This reduces correlation between trees, improving generalization and reducing overfitting.
Real-world example
Used in fraud detection systems for robust classification of transactions.
Common mistakes
- Assuming Random Forest uses full feature set at every split.
Follow-up questions
- Why does feature randomness help?
- Is Random Forest sensitive to scaling?