CatBoost is well covered with educational materials for both novice and advanced machine learners and data scientists.
This tutorial gives a short introduction to CatBoost and showcases its' functionality in Jupyter Notebook.
Getting started tutorials
These Python tutorials show how to start working with CatBoost.
Perform the following steps to use them:
Download the tutorials using one of the following methods:
- Click the Download button on the github page
- Clone the whole repository using the following command:
git clone https://github.com/catboost/tutorials
Run Jupyter Notebook in the directory with the required
CatBoost on GPU
This tutorial shows how to run CatBoost on GPU with Google Colaboratory.
Tutorials in the CatBoost repository
The CatBoost repository contains several tutorials on various topics, including but no limited to:
- how to apply the model
- how to use custom losses
- how to train a ranking model
- how to perform hyperparameter search
Coursera provides several lectures and a programming assignment as a part of the How to Win a Data Science Competition: Learn from Top Kagglers specialization.
The assignment helps to explore all basic functions of the CatBoost Python package.
The results can be submitted to Coursera for further validation.
Applying CatBoost models in ClickHouse
The ClickHouse documentation contains a tutorial on applying a CatBoost model in ClickHouse.