CatBoost is an open-source machine learning library developed by Yandex. It is based on gradient boosting over decision trees and is designed to handle categorical features automatically, making it easier to build accurate models with minimal preprocessing.

Features of CatBoost:

• Handles categorical features without manual encoding.
• Supports classification, regression, and ranking tasks.
• High accuracy with fast training on CPU and GPU.
• Integrates easily with Python, R, and other ML tools.
• Open-source and production-ready with model interpretability tools.

To install CatBoost: pip show catboost

Disclaimer: CatBoost is an independent open-source project developed by Yandex and is not affiliated with scikit-learn, XGBoost, or LightGBM. It is provided "as is," without any warranty, express or implied. Users are responsible for understanding and complying with licensing terms, data policies, and regulations while deploying CatBoost.