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