Language | English |
---|---|
ISBN-13 | 9781839213809 |
No of pages | 310 |
Book Publisher | Packt Publishing |
Published Date | 16 Oct 2020 |
Corey Wade, M.S. Mathematics, M.F.A. Writing & Consciousness, is the director and founder of Berkeley Coding Academy where he teaches Python, Data Science, Machine Learning, and AI to teens from all over the world. His books include The Python Workshop and Hands-on Gradient Boosting with XGBoost and scikit-learn. Wade's data science research and publications may be found on Towards Data Science, Springboard, and Medium. berkeleycodingacademy.com, coreyjwade.com
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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
Who this book is for
This book is for data science professionals and enthusiasts, data analysts, and developers who want to build fast and accurate machine learning models that scale with big data. Proficiency in Python, along with a basic understanding of linear algebra, will help you to get the most out of this book.