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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18. Other Books You May Enjoy

Metrics for Model Evaluations and Comparisons

In this chapter, we’ll measure how well a model is working for your dataset and how to adjust modeling parameters to improve the predictions. First, we will get hands-on experience with scikit-learn and the Python APIs for XGBoost. Then, we will learn about hyperparameter tuning and using metrics to measure how well a model is working. We learned about using some of these model-fitting metrics (such as R2 and RSME) in Chapter 5. We will expand on that understanding to include metrics for evaluating classification models in this chapter. Lastly, we will share cautions on over- and underfitting.

In this chapter, we will cover the following main topics:

Working with the XGBoost API
Evaluating model performance for classification and regression
Tuning XGBoost hyperparameters to improve model fit and efficiency

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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

Metrics for Model Evaluations and Comparisons

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Authors (2)

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