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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

Chapter 7: Discovering Exoplanets with XGBoost

In this chapter, you will journey through the stars in an attempt to discover exoplanets with XGBClassifier as your guide.

The reason for this chapter is twofold. The first is that it's important to gain practice in a top-to-bottom study using XGBoost since for all practical purposes, that is what you will normally do with XGBoost. Although you may not discover exoplanets with XGBoost on your own, the strategies that you implement here, which include choosing the correct scoring metric and carefully fine-tuning hyperparameters with that scoring metric in mind, apply to any practical use of XGBoost. The second reason for this particular case study is that it's essential for all machine learning practitioners to be proficient at competently handling imbalanced datasets, which is the key theme of this particular chapter.

Specifically, you will gain new skills in using the confusion matrix and the classification report, understanding...

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