XGBoost for Regression Predictive Modeling and Time Series Analysis

Learn how to build, evaluate, and deploy predictive models with expert guidance

Partha Pritam Deka, Joyce Weiner

• Publisher: De Gruyter
• Year: 2025
• Topic: Computer Science
• eBook ISBN:
  9781805129608
• Subtopic: Data Processing
• Index: Computer Science

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Table of contents

• Cover
• Title Page
• Copyright & Credits
• Foreword
• Contributors
• Table of Contents
• Preface
• Part 1: Introduction to Machine Learning and XGBoost with Case Studies
• Chapter 1: An Overview of Machine Learning, Classification, and Regression
• Chapter 2: XGBoost Quick Start Guide with an Iris Data Case Study
• Chapter 3: Demystifying the XGBoost Paper
• Chapter 4: Adding on to the Quick Start – Switching out the Dataset with a Housing Data Case Study
• Part 2: Practical Applications – Data, Features, and Hyperparameters
• Chapter 5: Classification and Regression Trees, Ensembles, and Deep Learning Models – What’s Best for Your Data?
• Chapter 6: Data Cleaning, Imbalanced Data, and Other Data Problems
• Chapter 7: Feature Engineering
• Chapter 8: Encoding Techniques for Categorical Features
• Chapter 9: Using XGBoost for Time Series Forecasting
• Chapter 10: Model Interpretability, Explainability, and Feature Importance with XGBoost
• Part 3: Model Evaluation Metrics and Putting Your Model into Production
• Chapter 11: Metrics for Model Evaluations and Comparisons
• Chapter 12: Managing a Feature Engineering Pipeline in Training and Inference
• Chapter 13: Deploying Your XGBoost Model
• Index
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