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Hands-On Gradient Boosting with XGBoost and scikit-learn

You're reading from Hands-On Gradient Boosting with XGBoost and scikit-learn Perform accessible machine learning and extreme gradient boosting with Python

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781839218354

Length 310 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Corey Wade

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1: Bagging and Boosting

2. Chapter 1: Machine Learning Landscape FREE CHAPTER

3. Chapter 2: Decision Trees in Depth

4. Chapter 3: Bagging with Random Forests

5. Chapter 4: From Gradient Boosting to XGBoost

6. Section 2: XGBoost

7. Chapter 5: XGBoost Unveiled

8. Chapter 6: XGBoost Hyperparameters

9. Chapter 7: Discovering Exoplanets with XGBoost

10. Section 3: Advanced XGBoost

11. Chapter 8: XGBoost Alternative Base Learners

12. Chapter 9: XGBoost Kaggle Masters

13. Chapter 10: XGBoost Model Deployment

14. Other Books You May Enjoy

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Technical requirements

The code for this chapter is available at https://github.com/PacktPublishing/Hands-On-Gradient-Boosting-with-XGBoost-and-Scikit-learn/tree/master/Chapter03

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Authors (1)

Wade

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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