pavelelk / tutorials

CatBoost tutorials repository

CatBoost tutorials

Basic

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

Python

• 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

• 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

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

Classification

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

Ranking

• Ranking Tutorial
  • CatBoost is learning to rank on Microsoft dataset (msrank).

Feature selection

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

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

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

Apply model

• 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.

• Export CatBoost Model in JSON format Tutorial

  • Catboost model could be saved in JSON format and applied.

Tools

• CatBoost on Google Colaboratory
  • Learn how to run CatBoost on CPU and GPU on Google Colaboratory and why GPU is the best choice.

Competition examples

• 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.

Events

• PyData Moscow tutorial

  • Tutorial from PyData Moscow, October 13, 2018.

• PyData NYC tutorial

  • Tutorial from PyData New York, October 19, 2018.

• PyData LA tutorial

  • Tutorial from PyData Los Angeles, October 21, 2018.

Tutorials on Russian

• Find tutorials on Russian language on the separate page.