CatBoost tutorials repository
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Latest commit 92f5b7b Feb 14, 2019

CatBoost tutorials


It's better to start CatBoost exploring from this basic tutorials.


  • Python Tutorial
    • This tutorial shows some base cases of using CatBoost, such as model training, cross-validation and predicting, as well as some useful features like early stopping, snapshot support, feature importances and parameters tuning.
  • Python Tutorial with task
    • There are 17 questions in this tutorial. Try answering all of them, this will help you to learn how to use the library.


  • R Tutorial
    • This tutorial shows how to convert your data to CatBoost Pool, how to train a model and how to make cross validation and parameter tunning.

Command line


  • Classification Tutorial
    • Here is an example for CatBoost to solve binary classification and multi-classification problems.


Feature selection

Model analysis

  • Object Importance Tutorial

    • This tutorial shows how to evaluate importances of the train objects for test objects. And with using of importance scores detect noisy train objects.
  • SHAP Values Tutorial

    • This tutorial shows how to use SHAP python-package to get and visualize feature importances.

Custom loss

Apply model


Competition examples


Tutorials on Russian

  • Find tutorials on Russian language on the separate page.