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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

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781839218354
Length 310 pages
Edition 1st Edition
Languages
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Author (1):
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Corey Wade Corey Wade
Author Profile Icon Corey Wade
Corey Wade
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Toc

Table of Contents (15) Chapters Close

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

Section 3: Advanced XGBoost

Building advanced XGBoost models takes practice, analysis, and experimentation. In this section, you will experiment with and fine-tune alternative base learners, learn innovative tips and tricks from Kaggle masters—including stacking and advanced feature engineering—and practice building robust models ready for industry deployment using sparse matrices, customized transformers, and pipelines.

This section comprises the following chapters:

  • Chapter 8, XGBoost Alternative Base Learners

  • Chapter 9, XGBoost Kaggle Masters

  • Chapter 10, XGBoost Model Deployment

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