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

Product type Paperback

Published in Dec 2024

Publisher Packt

ISBN-13 9781805123057

Length 308 pages

Edition 1st Edition

Concepts

Data Governance

Authors (2):

Joyce Weiner

Partha Pritam Deka

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Table of Contents (19) Chapters

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

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Technical requirements

You can continue using the same virtual environment that you set up earlier in this book since it already contains the necessary packages. The code samples for this chapter can be found in this book’s GitHub repository: https://github.com/PacktPublishing/XGBoost-for-Regression-Predictive-Modeling-and-Time-Series-Analysis.

For this chapter, you’ll be using the following tools and libraries:

Windows Subsystem for Linux (WSL)
Python 3.9
XGBoost 1.7.3
NumPy 1.21.5
pandas 1.4.2
scikit-learn 1.4.2
Seaborn
Anaconda
VS Code
Dask
Docker
Flask 3.0
Gunicorn

Using Linux for Deployment

When deploying your XGBoost model, it’s advisable to use Linux for your container environment. Linux is the most widely adopted operating system for distributed computing and offers greater flexibility for deployment across multiple cloud providers. If you’re developing on a Windows-based machine...

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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

Table of Contents (19) Chapters

Technical requirements

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Authors (2)

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