How do you tune XGBoost at scale without overfitting?hard
Answer
Key params: max_depth (keep 3-6), min_child_weight (higher = less overfit), subsample and colsample_bytree (0.7-0.9), small learning_rate + more rounds, lambda/alpha for L2/L1 reg.
Explanation
Key params: max_depth (keep 3-6), min_child_weight (higher = less overfit), subsample and colsample_bytree (0.7-0.9), small learning_rate + more rounds, lambda/alpha for L2/L1 reg. Use early stopping on a validation set. For scale: Bayesian optimization (Optuna) instead of grid search. Always monitor train vs val loss gap.
Follow-upCan you give a production example?