Features

1

Great quality without parameter tuning

Reduce time spent on parameter tuning, because CatBoost provides great results with default parameters

2

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.

3

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.

4

Improved accuracy

Reduce overfitting when constructing your models with a novel gradient-boosting scheme.

5

Fast prediction

Apply your trained model quickly and efficiently even to latency-critical tasks using CatBoost's model applier

About

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.

Benchmarks