CatBoost is a high-performance open source library for gradient boosting on decision trees
Great quality without parameter tuning
Reduce time spent on parameter tuning, because CatBoost provides great results with default parameters
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.
Fast and scalable GPU version
Train your model on a fast implementation of gradient-boosting algorithm for GPU. Use a multi-card configuration for large datasets.
Reduce overfitting when constructing your models with a novel gradient-boosting scheme.
Apply your trained model quickly and efficiently even to latency-critical tasks using CatBoost's model applier
CatBoost is an algorithm for gradient boosting on decision trees. It is developed by Yandex researchers and engineers, and is used for search, recommendation systems, personal assistant, self-driving cars, weather prediction and many other tasks at Yandex and in other companies, including CERN, Cloudflare, Careem taxi. It is in open-source and can be used by anyone.