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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Data-cleaning methods

Now that you have examined the dataset for problems, you need to address and correct the problems you’ve found. This is commonly the most time-consuming part of a project. When doing data cleaning, keep all the existing data and work in a new column when changing values (cleaning data). If dropping rows, create a new version of the data table. This allows you to go back to the original data easily if needed and gives you options when doing feature engineering, which you will learn about in Chapter 7.

Split your dataset before you clean the data to avoid inflating model accuracy

Before you do any data cleaning, split your data into training and test sets. This prevents leaking information from the training set into the testing set, which will inflate the results of accuracy tests. For example, if you calculate a mean of the dataset in full before you do the train/test split and have that data in a column in both datasets, you’ve provided the...

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Table of Contents (19) Chapters

Data-cleaning methods

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