Hands-On Gradient Boosting with XGBoost and scikit-learn

By Corey Wade
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About this book

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 starts with an introduction to machine learning and XGBoost before gradually moving on to gradient boosting. You’ll cover decision trees in detail and analyze bagging in the machine learning context. You’ll then learn how to build gradient boosting models from scratch and extend gradient boosting to big data to recognize their limitations. The book also shows you how to implement fast and accurate machine learning models using XGBoost and scikit-learn and takes you through advanced XGBoost techniques by focusing on speed enhancements, deriving parameters mathematically, and building robust models. With the help of detailed case studies, you’ll practice building and fine-tuning regressors and classifiers and become familiar with new tools such as feature importance and the confusion matrix. Finally, you’ll explore alternative base learners, learn invaluable Kaggle tricks such as building non-correlated ensembles and stacking, and prepare XGBoost models for industry deployment with unique 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.

Publication date:
October 2020
Publisher
Packt
Pages
243
ISBN
9781839218354

About the Author

  • Corey Wade

    Corey Wade, M.S. Mathematics, M.F.A. Writing & Consciousness, is the founder and director of Berkeley Coding Academy where he teaches Machine Learning and AI to teens from all over the world. Additionally, Corey chairs the Math Department at Berkeley Independent Study where he has received multiple grants to run after-school coding programs to help bridge the tech skills gap. Additional experiences include teaching Natural Language Processing with Hello World, developing Data Science curricula with Pathstream, and publishing statistics and machine learning models with Towards Data Science, Springboard, and Medium.

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