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

Common feature engineering techniques for numerical features

In the previous section, you learned how to handle missing data. In this section, you’ll learn about feature engineering techniques that are commonly applied to numeric variables. These techniques do things such as transform data so that it’s more conducive to modeling. Let’s get started.

Performing a log transform

The log transformation is a powerful technique, particularly for transforming a variable into a normal distribution. In this section, you’ll apply log transformation to positive numerical variables that exhibit non-normal distributions within the house prices dataset. You can refer to the data description provided on Kaggle for a comprehensive understanding of the variables that are subjected to log transformation. Let’s get started:

To get started, plot the variables you’ll transform to see how they don’t follow a normal distribution initially. You...

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

Common feature engineering techniques for numerical features

Performing a log transform

Unlock this book and the full library FREE for 7 days

Authors (2)

Personalised recommendations for you