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

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

Technical requirements

To effectively follow along with the code examples and exercises in this chapter on categorical encoding techniques, you will need to have the following Python libraries. The code provided in this chapter consists of Python functions designed to implement various encoding techniques for categorical data. All code examples and datasets used in this chapter are available on our GitHub repository at https://github.com/PacktPublishing/XGBoost-for-Regression-Predictive-Modeling-and-Time-Series-Analysis/blob/main/ch8/categorical_encoding.ipynb.

You will need the following Python packages:

As in Chapter 7, you will be using the Housing Price dataset. Access is available through the Kaggle platform at the following link: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data. To gain an understanding of the variables within the dataset, you can refer to...

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