catboost/tutorials

What it solves

This repository provides a comprehensive collection of learning materials for CatBoost, a high-performance library for gradient boosting. It helps users understand how to train models, tune parameters, evaluate performance, and deploy models across various environments and programming languages.

How it works

The tutorials are organized into practical, hands-on examples ranging from basic model training to advanced model analysis and deployment. They cover multiple interfaces including Python, R, command-line tools, and various programming languages like Java, Rust, and C++. Users can follow step-by-step notebooks to learn how to handle classification and ranking tasks, perform feature selection, and export models for production use.

Who it’s for

These tutorials are designed for data scientists, machine learning engineers, and developers who want to master CatBoost. Whether you are a beginner looking to understand the basics of gradient boosting or an experienced practitioner aiming to optimize model performance, analyze feature importance, or integrate CatBoost models into production applications, these resources provide the necessary guidance.

Highlights

• Comprehensive guides for Python, R, and command-line usage.
• Advanced model analysis tools, including SHAP values, object importance, and decision tree visualization.
• Practical deployment examples for mobile (CoreML), C++, Python, Java, and Rust.
• Specialized tutorials for custom metrics, ranking tasks, and feature selection.
• Real-world competition examples from Kaggle to demonstrate effective model application.