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Key benefits

Get up and running with machine learning and understand how to boost models with XGBoost in no time

Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results

Discover tips and tricks and gain innovative insights from XGBoost Kaggle winners

Description

XGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently. The book introduces machine learning and XGBoost in scikit-learn before building up to the theory behind gradient boosting. You’ll cover decision trees and analyze bagging in the machine learning context, learning hyperparameters that extend to XGBoost along the way. You’ll build gradient boosting models from scratch and extend gradient boosting to big data while recognizing speed limitations using timers. Details in XGBoost are explored with a focus on speed enhancements and deriving parameters mathematically. With the help of detailed case studies, you’ll practice building and fine-tuning XGBoost classifiers and regressors using scikit-learn and the original Python API. You'll leverage XGBoost hyperparameters to improve scores, correct missing values, scale imbalanced datasets, and fine-tune alternative base learners. Finally, you'll apply advanced XGBoost techniques like building non-correlated ensembles, stacking models, and preparing models for industry deployment using sparse matrices, customized transformers, and pipelines. By the end of the book, you’ll be able to build high-performing machine learning models using XGBoost with minimal errors and maximum speed.

What you will learn

Build gradient boosting models from scratch

Develop XGBoost regressors and classifiers with accuracy and speed

Analyze variance and bias in terms of fine-tuning XGBoost hyperparameters

Automatically correct missing values and scale imbalanced data

Apply alternative base learners like dart, linear models, and XGBoost random forests

Customize transformers and pipelines to deploy XGBoost models

Build non-correlated ensembles and stack XGBoost models to increase accuracy

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Machine Learning with PyTorch and Scikit-Learn

$54.99

Machine Learning for Algorithmic Trading

$57.99

Hands-On Gradient Boosting with XGBoost and scikit-learn

$52.99

Total $ 165.97

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Amazon verified reviews

Justin R McCormick Sep 22, 2021

I have dabbled here and there in Machine Learning/Neural Networks. This book has brought more of an understanding to the concepts for me before page 100. Recommended.

Amazon Verified review

Omar Kamal Hosney May 17, 2021

That is one of the best books that I have ever read.

Peter Grace Dec 11, 2021

The writing style here where you build and step through sklearn trees to XGBoost is superb. I haven’t read another ml text that flows as well as this. XGBoost is arguably the tool for tabular data and this book will get you applying it to good purpose, fast. I hope Corey can find the time to keep publishing more books like this to continue to help make accessible the important topics of machine learning and data insight.

Justin Nov 23, 2023

If you’re just starting out with XGBoost or machine learning, you will not regret purchasing this book. The theory + code makes this a must have. Wish I would’ve found it sooner!

Pujun Mar 04, 2021

I think the book is great for anyone who wants to get to speed about the fundamental techniques related to Gradient boosting and also learn about scikit-learn. Complete disclosure: I have a graduate degree in computer science ( AI ) and I currently work as a Machine Learning engineer in the Silicon Valley. I read the book for learning scikit-learn and also just refresh some of the concepts about boosting that I had learned during graduate school but had not used them in the last 2 years or so.The book has plenty of examples and resources that not only show how to implement the algorithm but also goes over when is the right time to use such an algorithm ( the pros and cons of the approaches ) and also how to implement it using scikit-learn, a pretty popular python package.Would definitely recommend the book

Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python

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