gradient-boosting

• XGBoost
• LightGBM
• CatBoost

Description: Gradient boosting builds models sequentially to correct errors made by previous models, optimizing for accuracy.

Key Points:

• Highly accurate and efficient.
• Can handle different types of data.
• Prone to overfitting if not properly tuned.

Applications: Web search ranking, customer churn prediction, insurance risk prediction.