Effective XGBoost: Optimizing, Tuning, Understanding, and Deploying Classification Models (Treading on Python) - Softcover

Harrison, Matt

Softcover

ISBN 10: 1792310390 ISBN 13: 9781792310393

Publisher: MetaSnake, 2023

Synopsis

"Effective XGBoost" is the ultimate guide to mastering the art of classification. Whether you're a seasoned data scientist or just starting out, this comprehensive book will take you from the basics of XGBoost to advanced techniques for optimizing, tuning, understanding, and deploying your models.

XGBoost is one of the most popular machine learning algorithms used in data science today. With its ability to handle large datasets, handle missing values, and deal with non-linear relationships, it has become an essential tool for many data scientists. In this book, you'll learn everything you need to know to become an expert in XGBoost.

Starting with the basics, you'll learn how to use XGBoost for classification tasks, including how to prepare your data, select the right features, and train your model. From there, you'll explore advanced techniques for optimizing your models, including hyperparameter tuning, early stopping, and ensemble methods.

But "Effective XGBoost" doesn't stop there. You'll also learn how to interpret your XGBoost models, understand feature importance, and deploy your models in production. With real-world examples and practical advice, this book will give you the skills you need to take your XGBoost models to the next level.

Whether you're working on a Kaggle competition, building a recommendation system, or just want to improve your data science skills, "Effective XGBoost" is the book for you. With its clear explanations, step-by-step instructions, and expert advice, it's the ultimate guide to mastering XGBoost and becoming a top-notch data scientist.

Edition details

• Publisher: MetaSnake
• Publication date: 2023
• ISBN 10: 1792310390
• ISBN 13: 9781792310393
• Binding: Paperback
• Language: English
• Number of pages: 220
• Editor: Krueger Edward, Rook Alex, Legere Ronald