CatBoost is a fast, scalable, high performance open-source gradient boosting on decision trees libraryGet started
On December 2018, on NeurIPS conference in Montreal, Yandex team presented two papers related to CatBoost, an open-source machine learning library developed by Yandex.
CatBoost team continues to make a lot of improvements and speedups. What new and interesting have we added in our two latest releases and why is it worth to try CatBoost now? We'll discuss it in this post.
New superb tool for exploring feature importance, new algorithm for finding most influential training samples, possibility to save your model as cpp or python code and more. Check CatBoost v0.8 details inside!
Come and listen our talk about the fastest implementation of Gradient Boosting for GPU at the GTC 2018 Silicon Valley! GTC will take place on March 26-29 and will provide an excellent opportunity to get more details about CatBoost performance on GPU.
New version of CatBoost has industry fastest inference implementation. It's 35 times faster than open-source alternatives and completely production ready. Furthermore 0.6 release contains a lot of speedups and improvements. Find more inside.
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.