Get a principled, step-by-step gradient-boosting tuning plan that beats default settings without overfitting your validation set.
## CONTEXT Gradient-boosted trees remain the default winner on tabular data in 2026, and XGBoost, LightGBM, and CatBoost are the workhorses. Yet most practitioners either accept library defaults or run an unfocused grid that wastes compute and overfits the validation split. Effective tuning follows an order: fix a…
Premium Prompt
Unlock this prompt — and all 30,000+ expert-crafted prompts — with Pro.
Unlock with Pro