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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 Tutorial
- This tutorial shows how to train and apply model with the command line tool.

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

Feature selection Tutorial
- This tutorial shows how to make feature evaluation with CatBoost and explore learning rate.

Object Importance Tutorial
- This tutorial shows how to evaluate importances of the train objects for test objects, and how to detect broken train objects by using the importance scores.
SHAP Values Tutorial
- This tutorial shows how to use SHAP python-package to get and visualize feature importances.
Export CatBoost Model in JSON format Tutorial
- This tutorial shows how to save catboost model in JSON format and apply it.
Visualization of CatBoost decision trees tutorial
- This tutorial shows how to visualize catboost decision trees.
Feature statistics tutorial
- This tutorial shows how to calculate feature statistics for catboost model.
CatBoost PredictionDiff Feature Importance Tutorial
- This tutorials shows how to use PredictionDiff feature importances.

Custom Metrics Tutorial
- This tutorial shows how to add custom per-object metrics.

CatBoost CoreML Tutorial
- Explore this tutorial to learn how to convert CatBoost model to CoreML format and use it on any iOS device.
Export CatBoost Model as C++ code Tutorial
- Catboost model could be saved as standalone C++ code.
Export CatBoost Model as Python code Tutorial
- Catboost model could be saved as standalone Python code.
Apply CatBoost model from Java
- Explore how to apply CatBoost model from Java application. If you just want to look at code snippets you can go directly to CatBoost4jPredictionTutorial.java
Apply CatBoost model from Rust
- Explore how to apply CatBoost model from Rust application. If you just want to look at code snippets you can go directly to main.rs
Convert LightGBM to CatBoost to use CatBoost fast appliers
- Convert LightGBM to CatBoost, save resulting CatBoost model and use CatBoost C++, Python, C# or other applier, which in case of not symmetric trees will be around 7-10 faster than native LightGBM one.
- Note that CatBoost applier with CatBoost models is even faster, because it uses specific fast symmetric trees.

Gradient Boosting: CPU vs GPU
- This is a basic tutorial which shows how to run gradient boosting on CPU and GPU on Google Colaboratory.
Regression on Gradient Boosting: CPU vs GPU
- This is a basic tutorial which shows how to run regression on gradient boosting on CPU and GPU on Google Colaboratory.

Kaggle Paribas Competition Tutorial
- This tutorial shows how to get to a 9th place on Kaggle Paribas competition with only few lines of code and training a CatBoost model.
ML Boot Camp V Competition Tutorial
- This is an actual 7th place solution by Mikhail Pershin. Solution is very simple and is based on CatBoost.
CatBoost & TensorFlow Tutorial
- This tutorial shows how to use CatBoost together with TensorFlow on Kaggle Quora Question Pairs competition if you have text as input data.