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

Packaging a model for production deployment

So far in this chapter, you’ve been interacting with the models you’ve developed directly within Python by making calls to functions and methods that train a model or use a model to make a prediction. In a production environment, you won’t want to make Python calls directly. You’ll either want to create a web application that interfaces with your model, or you’ll want to use an application programming interface (API). An API allows a user to access the functions and methods they need to train and use your model without having to know how to code in Python. Building an API allows a user to call your model from their web application. To better understand this, let’s look at the parts of a model that’s running in a production environment:

Figure 13.1 – Components of a model running in a production environment

Figure 13.1 shows your model inside an API framework...

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

Packaging a model for production deployment

Unlock this book and the full library FREE for 7 days

Authors (2)

Personalised recommendations for you