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

Implementing LIME for model interpretation

Local Interpretable Model-agnostic Explanations (LIME) uses a different approach from SHAP. While SHAP assigns each feature a contribution score to explain the model’s prediction, LIME approaches interpretability by building a simple, local model around the prediction. LIME adjusts the input features in small, controlled ways and observes how these changes influence the prediction, enabling it to construct a straightforward model that explains the prediction in an interpretable manner.

Why use LIME?

LIME is particularly useful for debugging individual predictions or edge cases. It focuses on local explanations. Additionally, LIME works with any machine learning model, not just tree-based models such as XGBoost.

Using LIME to interpret XGBoost predictions

To use LIME effectively, you’ll need to pass unscaled data to LIME and use a wrapper function to scale the data before prediction. Let’s try it out:

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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

Implementing LIME for model interpretation

Why use LIME?

Using LIME to interpret XGBoost predictions

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Authors (2)

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