XGBoost

XGBoost, short for Extreme Gradient Boosting, builds an ensemble of decision trees in sequence. Each new tree concentrates on errors left by the trees already created, and the combined prediction can capture complex relationships in structured or tabular data. The implementation is popular for classification, regression, ranking, and forecasting because it includes regularization, missing-value handling, parallel computation, and extensive tuning controls. Strong performance is not automatic: feature leakage, weak validation, imbalanced targets, or excessively deep trees can produce misleading results. Feature importance also shows association rather than cause. Production use requires reproducible preprocessing, representative evaluation, calibrated outputs where needed, and monitoring when data patterns change.