Boosting: each next model fixes the previous one’s mistakes

Bagging builds trees independently and averages. Boosting builds sequentially: each new tree focuses on the examples or residuals where the previous ones failed. That’s the fundamental difference. Boosting is more powerful but more prone to overfitting. XGBoost, LightGBM, CatBoost — all built on this idea and today dominate tabular data.

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