CatBoost is an open-source gradient boosting library 
with categorical features support

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Reduced overfitting
Achieve better results by reducing overfitting with CatBoost that is based on 
a proprietary algorithm for constructing models that differs from the standard gradient-boosting scheme.
Categorical features support
Improve your training results with CatBoost that allows you to use non-numeric factors, instead of having to pre-process your data or spend time and effort turning it to numbers.
User-friendly API interface
Launch CatBoost right from the command line or enjoy a user-friendly API for Python or R, with tools for formula analysis and training visualisation.


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.


Extremely fast learning on GPU has arrived!
CatBoost version 0.3 brings efficient support of distributed training on GPU! One server with 8 GPUs can process as much data as few hundreds of CPU servers and will work much faster. Even with a single GPU you will get up to 40x speed up of your training. Check out our benchmarks inside and download new version on GitHub.
Version 0.2 released
We are proud to release CatBoost version 0.2. Speed, stability, quality and ton of other improvements are already published on GitHub. Find the full list of improvements below.
CatBoost at ICML 2017
Come and meet us at the 2017 ICML conference in Sydney! The 34th International Conference on Machine Learning will take place on August 6-11 and will provide an excellent opportunity to get a demo of CatBoost in action.

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