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

View More author details

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

Why subscribe?

18. Other Books You May Enjoy

Downloading and installing the XGBoost package

The first thing you need to do is prepare your Python environment. We recommend using Anaconda to install and manage your Python packages because it does a good job of handling any package-to-package dependencies. We’ll also be using a virtual environment to keep all the required packages for the projects in this book contained in a single place.

Anaconda and conda are recommended

Anaconda checks for package dependencies during the installation process, which is important. With Python, a single package may require others to be installed and may require a specific version of those packages. For example, pandas requires NumPy. When you use Anaconda, these version checks are done at your environment level, including everything you have already installed. This avoids a problem where you might have conflicting versions. However, it can sometimes take a while for it to build a solution that addresses all the packages you’...

The rest of the chapter is locked

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

Downloading and installing the XGBoost package

Unlock this book and the full library FREE for 7 days

Authors (2)

Personalised recommendations for you