Gradient Boosting

Gradient Boosting Methods

Gradient boosting builds trees sequentially, each correcting previous errors. It often achieves excellent performance.

XGBoost

XGBoost provides efficient gradient boosting. Parameters: n_estimators, learning_rate, max_depth, subsample.

Early stopping prevents overfitting. eval_set monitors validation performance.

LightGBM

LightGBM is faster than XGBoost for large datasets. Uses histogram-based splitting.

Parameters: num_leaves, learning_rate, n_estimators. GBDT boosting type.

CatBoost

CatBoost handles categorical features natively. Ordered boosting reduces prediction shift.

Key Takeaways

1. Gradient boosting is powerful for tabular data
2. XGBoost, LightGBM, CatBoost are popular implementations
3. Key: learning_rate, n_estimators, max_depth