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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

Searching for exoplanets

In this section, we'll begin the search for exoplanets by analyzing the Exoplanets dataset. We'll provide historical context for the discovery of exoplanets before attempting to detect them via plotting and observing light graphs. Plotting time series is a valuable machine learning skill that may be used to gain insights into any time series datasets. Finally, we'll make initial predictions using machine learning before revealing a glaring shortcoming.

Historical background

Astronomers have been gathering information from light since antiquity. With the advent of the telescope, astronomical knowledge surged in the 17th century. The combination of telescopes and mathematical models empowered 18th-century astronomers to predict planetary locations and eclipses within our own solar system with great precision.

In the 20th century, astronomical research continued with more advanced technology and more complex mathematics. Planets revolving...

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