Angielski
This book on Gradient Boosting methods is intended for students, academics, engineers, and data scientists who wish to discover in depth the functioning of this powerful Machine Learning method.
All the concepts are illustrated by samples of code. They allow the reader to build from scratch their training library of Gradient Boosting. In parallel, the book presents the best practices of Data Science and provides the reader with a solid technical and mathematical background to build Machine Learning models.
After a presentation of the principles of Gradient Boosting, its use cases, advantages, and limitations, the reader is introduced to the details of the mathematical theory. A simple but complete implementation is given to illustrate how it works.
The reader is then armed to tackle the application and configuration of this method. Data preparation, training, model explanation, automatic Hyper Parameter Tuning, and use of objective functions are covered in detail!
The book's last chapters extend the subject to the application of Gradient Boosting to time series, the presentation of the emblematic libraries XGBoost, CatBoost, and LightGBM as well as the concept of multi-resolution models.
