Your question is XGBoost Overview. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
What is XGBoost? Explain it in detail.
Describe how gradient-boosted decision trees work, including regularization, objective functions, learning rate, tree depth, subsampling, and missing-value handling. Explain how you would build a reliable batch pipeline that validates features and labels, trains an XGBoost model, evaluates it, stores the artifact, and supports reproducible retraining.