Which of the following algorithms is a 'boosting' ensemble method?

Boosting trains models sequentially as each new model focuses on correcting the errors of the previous ones, reducing bias. Boosting Algorithms include AdaBoost (adjusts sample weights), Gradient Boosting (fits residuals), XGBoost (regularized gradient boosting — fast, handles missing values), LightGBM (leaf-wise growth — faster), CatBoost (handles categorical features). Bagging (Random Forest) trains models in parallel on random subsets which reduces variance. XGBoost and LightGBM dominate banking ML competitions (Kaggle) and are widely used in credit scoring, churn prediction, and fraud detection due to their high accuracy and built-in regularization.