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XGBoost: Gradient Boosting Deep Dive
Master XGBoost from first principles — gradient boosting intuition, the regularized objective with full derivations, split-finding algorithms, histogram approximations, SHAP values, hyperparameter tuning, and production patterns.
Why XGBoost Dominates Tabular Data
XGBoost (eXtreme Gradient Boosting) won more Kaggle competitions between 2014–2019 than any other algorithm. It builds decision trees sequentially — each new tree corrects the errors of all previous trees combined. Unlike Random Forest (independent parallel trees, then average), XGBoost uses gradient descent in function space: tree t fits the negative gradient of the loss evaluated at the current ensemble's predictions. Three innovations make it extreme: (1) a regularized objective that penalizes tree complexity, (2) a second-order Taylor approximation that makes optimization more principled, and (3) the histogram-based approximate split-finding that scales to billions of samples.
Gradient Boosting: Sequential Error Correction
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