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. Analyze your model and your data with CatBoost analyzing tools

Introducing CatBoost

Fri Oct 01 2021 22:05:27 GMT+0300 (Moscow Standard Time)