Welcome to the official GitHub repository for "Effective XGBoost". Here, you'll find all the code examples included in the book, neatly organized by chapter. This repository serves as a practical resource for readers and allows for active collaboration through GitHub.
"Effective XGBoost" is an in-depth, comprehensive guide to creating classification models, designed to help readers from a wide range of backgrounds, from beginners to seasoned professionals. It provides clear explanations, real-world examples, practical exercises, and much more.
This book is the culmination of years of experience and knowledge shared by the author, Matt Harrison, a data science and Python consultant and corporate trainer.
You'll find all code examples for the book here.
If you have not already done so, you can purchase "Effective XGBoost" from the following vendors:
- Digital PDF/epub/kindle Effective XGBoost Digital
- Physical Version Amazon
If you find the content of this repository helpful, imagine how much more you could learn from the complete book! Your purchase not only supports the work of the author but also contributes to the continuous improvement of this code repository.
We strive for perfection, but nobody's perfect. If you encounter any issues or errors in the book or in the code samples, please don't hesitate to file a bug in the Issues section of this repository. When filing an issue, please include as much detail as possible, such as the chapter and page number, description of the issue, and, if relevant, a screenshot or code snippet.
We welcome and appreciate contributions from our readers. If you've noticed an error or a way to improve the code, feel free to create a pull request. For significant changes, please open an issue first to discuss the proposed changes.
Happy coding, and enjoy the book!