Bagging (Random Forest) trains trees in parallel on random subsets and averages — reduces variance.

Bagging (Random Forest) trains trees in parallel on random subsets and averages — reduces variance. Boosting trains sequentially, each tree correcting previous errors — reduces bias. XGBoost adds L1/L2 regularization to the objective, handles missing values natively, and uses second-order gradients for faster convergence. Chosen for markdown because demand patterns have structured nonlinearities boosting captures well.