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XGBoost for Regression Predictive Modeling and Time Series Analysis

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

Technical requirements

This chapter, as with Chapter 2, is intended as a hands-on guide. You can use the same virtual environment already set up with the necessary packages in Chapter 2.

The code presented in this chapter is available on our GitHub repository: https://github.com/PacktPublishing/XGBoost-for-Regression-Predictive-Modeling-and-Time-Series-Analysis

You will use the following software and Python packages in this chapter:

Python 3.9
xgboost 1.7.3
numpy 1.21.5
pandas 1.4.2
scikit-learn 1.4.2
seaborn 0.12.2
Anaconda
VS Code

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Authors (2)

Partha Pritam Deka

Partha Pritam Deka

Partha Pritam Deka is a data science leader with 15+ years of experience in semiconductor supply chain and manufacturing. As a senior staff engineer at Intel, he has led AI and machine learning teams, achieving significant cost savings and optimizations. He and his team developed a computer vision system that improved Intel's logistics, earning CSCMP Innovation Award finalist recognition. An active AI community member, Partha is a senior IEEE member and speaker at Intel's AI Everywhere conference. He also reviews for NeurIPS, contributing to AI and analytics in semiconductor manufacturing.

See other products by Partha Pritam Deka

Joyce Weiner

Joyce Weiner

Joyce Weiner is a principal engineer with Intel Corporation. She has over 25 years of experience in the semiconductor industry, having worked in fabrication, assembly and testing, and design. Since the early 2000s, she has deployed applications that use machine learning. Joyce is a black belt in Lean Six Sigma and her area of technical expertise is the application of data science to improve efficiency. She has a BS in Physics from Rensselaer Polytechnic Institute and an MS in Optical Sciences from the University of Arizona.

See other products by Joyce Weiner

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