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XGBoost for Regression Predictive Modeling and Time Series Analysis

You're reading from   XGBoost for Regression Predictive Modeling and Time Series Analysis Learn how to build, evaluate, and deploy predictive models with expert guidance

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Product type Paperback
Published in Dec 2024
Publisher Packt
ISBN-13 9781805123057
Length 308 pages
Edition 1st Edition
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Authors (2):
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Joyce Weiner Joyce Weiner
Author Profile Icon Joyce Weiner
Joyce Weiner
Partha Pritam Deka Partha Pritam Deka
Author Profile Icon Partha Pritam Deka
Partha Pritam Deka
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1:Introduction to Machine Learning and XGBoost with Case Studies
2. Chapter 1: An Overview of Machine Learning, Classification, and Regression FREE CHAPTER 3. Chapter 2: XGBoost Quick Start Guide with an Iris Data Case Study 4. Chapter 3: Demystifying the XGBoost Paper 5. Chapter 4: Adding on to the Quick Start – Switching out the Dataset with a Housing Data Case Study 6. Part 2: Practical Applications – Data, Features, and Hyperparameters
7. Chapter 5: Classification and Regression Trees, Ensembles, and Deep Learning Models – What’s Best for Your Data? 8. Chapter 6: Data Cleaning, Imbalanced Data, and Other Data Problems 9. Chapter 7: Feature Engineering 10. Chapter 8: Encoding Techniques for Categorical Features 11. Chapter 9: Using XGBoost for Time Series Forecasting 12. Chapter 10: Model Interpretability, Explainability, and Feature Importance with XGBoost 13. Part 3: Model Evaluation Metrics and Putting Your Model into Production
14. Chapter 11: Metrics for Model Evaluations and Comparisons 15. Chapter 12: Managing a Feature Engineering Pipeline in Training and Inference 16. Chapter 13: Deploying Your XGBoost Model 17. Index 18. Other Books You May Enjoy

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