Bagging stands for Bootstrap Aggregating.
👉 How it works
Multiple subsets of data are created by random sampling with replacement (bootstrap sampling).
A base model (e.g., decision tree) is trained on each subset independently.
The final prediction is made by majority voting (for classification) or averaging (for regression).