juniorRandom Forest
What is bootstrap sampling?
Updated May 17, 2026
Short answer
Bootstrap sampling is randomly sampling data with replacement to create multiple training datasets.
Deep explanation
Each tree in Random Forest is trained on a bootstrap sample, meaning some rows may repeat while others may be left out. This introduces diversity among trees.
Real-world example
Used in ensemble models for medical diagnosis systems to improve robustness.
Common mistakes
- Assuming bootstrap samples are unique subsets.
Follow-up questions
- Why use replacement in sampling?
- What are out-of-bag samples?