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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18. Other Books You May Enjoy

Training the XGBoost model

With the data prepared, we can now train the XGBoost model. For time series forecasting, XGBoost typically uses the regression objective (reg:squarederror), which optimizes for minimizing squared errors. First, we will initialize the XGBoost model and train it on the training data. Then, we can use the model to make predictions on the test set. Let’s start:

Begin by importing XGBRegressor from xgboost and initializing the regressor with the objective of reg:squarederror. Start with n_estimators = 100, max_depth =3, and learning_rate = 0.1. These are conservative settings compared to the default XGBoost values, which will prevent overfitting of the data. You can tune these hyperparameters later to suit the project you are doing. Details on the XGBoost hyperparameters are in Chapter 5:
```
from xgboost import XGBRegressor
model = XGBRegressor(objective='reg:squarederror',
    n_estimators=100, max_depth=3, learning_rate...
```

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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

Training the XGBoost model

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