Explain XGBoost's Overfitting Resistance
Company: Bytedance
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
Explain why an XGBoost model is often less prone to overfitting than a single decision tree. Discuss the mechanisms in XGBoost that control model complexity and improve generalization, and mention cases where XGBoost can still overfit.
Quick Answer: This question evaluates understanding of ensemble learning and model generalization, focusing on why gradient-boosted tree ensembles are often less prone to overfitting than single decision trees.