juniorRandom Forest
What is bagging in Random Forest?
Updated May 17, 2026
Short answer
Bagging is a technique where multiple models are trained on different bootstrapped subsets of data.
Deep explanation
Bootstrap Aggregating (Bagging) helps reduce variance by training models on randomly sampled datasets with replacement. Random Forest uses bagging as its core mechanism along with feature randomness.
Real-world example
Used in predictive maintenance to stabilize predictions from noisy sensor data.
Common mistakes
- Confusing bagging with boosting.
Follow-up questions
- How does bagging reduce variance?
- What is bootstrap sampling?