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Machine Learning for Imbalanced Data

You're reading from  Machine Learning for Imbalanced Data

Product type Book
Published in Nov 2023
Publisher Packt
ISBN-13 9781801070836
Pages 344 pages
Edition 1st Edition
Languages
Authors (2):
Kumar Abhishek Kumar Abhishek
Profile icon Kumar Abhishek
Dr. Mounir Abdelaziz Dr. Mounir Abdelaziz
Profile icon Dr. Mounir Abdelaziz
View More author details

Table of Contents (15) Chapters

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

Technical requirements

This chapter will make use of common libraries such as matplotlib, seaborn, pandas, numpy, scikit-learn, and imbalanced-learn. The code and notebooks for this chapter can be found on GitHub at https://github.com/PacktPublishing/Machine-Learning-for-Imbalanced-Data/tree/master/chapter03. To run the notebook, there are two options: you can click the Open in Colab icon at the top of the chapter’s notebook, or you can launch it directly from https://colab.research.google.com using the GitHub URL of the notebook.

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