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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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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

Summary

In this chapter, you greatly extended your range of XGBoost by applying all XGBoost base learners, including gbtree, dart, gblinear, and random forests, to regression and classification datasets. You previewed, applied, and tuned hyperparameters unique to base learners to improve scores. Furthermore, you experimented with gblinear using a linearly constructed dataset and with XGBRFRegressor and XGBRFClassifier to build XGBoost random forests without any boosting whatsoever. Now that you have worked with all base learners, your comprehension of the range of XGBoost is at an advanced level.

In the next chapter, you will analyze tips and tricks from Kaggle masters to advance your XGBoost skills even further!

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