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Machine Learning for Imbalanced Data

You're reading from   Machine Learning for Imbalanced Data Tackle imbalanced datasets using machine learning and deep learning techniques

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781801070836
Length 344 pages
Edition 1st Edition
Languages
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Authors (2):
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Dr. Mounir Abdelaziz Dr. Mounir Abdelaziz
Author Profile Icon Dr. Mounir Abdelaziz
Dr. Mounir Abdelaziz
Kumar Abhishek Kumar Abhishek
Author Profile Icon Kumar Abhishek
Kumar Abhishek
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Toc

Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Introduction to Data Imbalance in Machine Learning FREE CHAPTER 2. Chapter 2: Oversampling Methods 3. Chapter 3: Undersampling Methods 4. Chapter 4: Ensemble Methods 5. Chapter 5: Cost-Sensitive Learning 6. Chapter 6: Data Imbalance in Deep Learning 7. Chapter 7: Data-Level Deep Learning Methods 8. Chapter 8: Algorithm-Level Deep Learning Techniques 9. Chapter 9: Hybrid Deep Learning Methods 10. Chapter 10: Model Calibration 11. Assessments 12. Index 13. Other Books You May Enjoy Appendix: Machine Learning Pipeline in Production

Technical requirements

In this chapter, we will utilize common libraries such as numpy, scikit-learn, and imbalanced-learn. The code and notebooks for this chapter are available on GitHub at https://github.com/PacktPublishing/Machine-Learning-for-Imbalanced-Data/tree/master/chapter02. You can just fire up the GitHub notebook using Google Colab by clicking on the Open in Colab icon at the top of this chapter’s notebook or by launching it from https://colab.research.google.com using the GitHub URL of the notebook.

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