CatBoost is an open-source gradient boosting library with categorical features support
CatBoost is an algorithm for gradient boosting on decision trees. Developed by Yandex researchers and engineers, it is the successor of the MatrixNet algorithm that is widely used within the company for ranking tasks, forecasting and making recommendations. It is universal and can be applied across a wide range of areas and to a variety of problems.
Works in Python
Here an overview and comparison between XGBOOST, Light GBM and Catboost