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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

Understanding costs in practice

We need to understand the various types of costs involved while creating weights for different classes. These costs change on a case-by-case basis. Let’s discuss an example of cost calculations to understand what we should consider while thinking about cost calculations.

Let’s take the example of pediatric pneumonia. According to UNICEF, a child dies of pneumonia every 43 seconds [4]. Imagine we are creating a new test for pediatric pneumonia – how will we decide the cost of different errors?

Let’s review the confusion matrix from Table 5.1. There will usually be no extra cost for True Negatives and True Positives. But using a False Negative – that is, when a child has pneumonia and predicting the child to be healthy – will have a very high cost. On the flip side, when a healthy child is predicted as being affected by pneumonia, there will be a cost associated with the troubles the family of the child may...

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