CatBoost is a state-of-the-art open-source gradient boosting on decision trees library.

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.

  • Accurate: leads or ties competition on standard benchmarks
  • Robust: reduces the need for extensive hyper-parameter tuning
  • Easy-to-use: offers Python interfaces integrated with scikit, as well as R and command-line interfaces
  • Practical: uses categorical features directly and scalably
  • Extensible: allows specifying custom loss functions

Get Started
  1. Read the documentation
  2. Train CatBoost using Python, R or command line
  3. Use CatBoost Viewer to analyze training process
Introducing CatBoost