What is bootstrapping in ensemble learning?

Bootstrapping is sampling data with replacement to create multiple training subsets.

Bootstrapping is a statistical technique where multiple datasets are created by randomly sampling with replacement from the original dataset. Each bootstrap sample is used to train a separate model. This introduces diversity among models in ensemble methods like bagging and Random Forest. The variability across models helps reduce overall variance when predictions are aggregated.