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

Tuning XGBoost hyperparameters to improve model fit and efficiency

In the previous section, we discussed various metrics to measure how well a model fits the dataset. Now, we will explore how to optimize an XGBoost model by tuning its hyperparameters to improve both accuracy and efficiency. Hyperparameter tuning is crucial in extracting the best performance from XGBoost, as it helps balance model complexity and overfitting, while also improving computation efficiency.

What is hyperparameter tuning?

Hyperparameter tuning is an optimization process that involves adjusting parameters that control the training process of the model. These parameters are not learned from the data but are set before the training process begins. By fine-tuning them, you can significantly improve the model’s performance.

There are several methods you can use to systematically tune these hyperparameters:

Grid search: This method tries every combination of hyperparameters in a specified...

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

Tuning XGBoost hyperparameters to improve model fit and efficiency

What is hyperparameter tuning?

Unlock this book and the full library FREE for 7 days

Authors (2)

Personalised recommendations for you