Effective XGBoost: Optimizing, Tuning, Understanding, and Deploying Classification Models

by Matt Harrison, Bojan Tunguz (Foreword), Edward Krueger (Editor)

"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.

220 pages, Paperback

Published March 21, 2023

Reviews

[Chao Chen · Rating 4 out of 5]

descent book, good for begineers

[Josua Naiborhu · Rating 5 out of 5]

I enjoyed reading this book and the way the author explains the theory and hand-on experience along with nice visualization brings this book to the next level.

[Andrew Somers · Rating 5 out of 5]

Very practical, useful reference!

[Harry Lawrence · Rating 5 out of 5]

Effective XGBoost takes you through an XGBoost classification model from start to finish in Python, using one single data source and providing a GitHub repo with all the necessary code to help you follow along.

There is a surprising amount of knowledge crammed into just over 200 pages, and I never felt like the short length left me short-changed in terms of detail or explanations, it's a fantastic example of how even highly technical books can be written concisely and with great focus.

The only blocker for some people will be that the author does assume an intermediate knowledge of Pandas, and Python more generally, but if you don't already have that then he has other books on those topics you could go through first. I haven't read any of the others but will be picking up Effective Pandas because this book showed me I have a lot more to learn in that area as well as in modelling with XGBoost.

If you are already comfortable with Python, this is an excellent resource for getting to know XGBoost, and I feel like I have picked up a lot of new skills and techniques to use on my next model, particularly when it comes to data visualisation and tuning the features that go into my models.