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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Classification and Regression Trees, Ensembles, and Deep Learning Models – What’s Best for Your Data?

This chapter covers when to use ensemble models or simple classification and regression tree models. Through experimenting with the housing dataset that we used in Chapter 4, you will see the differences between models in terms of model accuracy, as measured by R2 and RMSE, how long it takes for the models to be trained, and how long it takes for the models to make predictions. To do so, you will test and compare XGBoost, scikit-learn gradient boosting, and random forest models. You will learn when to use decision-tree-based machine learning versus deep learning. Lastly, you will learn how to set the various hyperparameters (parameters that control how the model learns) for XGBoost.

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

When to use ensemble models versus single Classification and Regression Tree (CART) models
Comparing models with the...

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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

Classification and Regression Trees, Ensembles, and Deep Learning Models – What’s Best for Your Data?

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Authors (2)

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