Machine learning for imbalanced data tackle imbalanced datasets using machine learning and deep learning techniques

As machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to suboptimal performance on imbalanced data. This compr...

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Bibliographic Details
Main Authors: Abhishek, Kumar, Abdelaziz, Mounir (Author)
Format: eBook
Language:English
Published: Birmingham, UK Packt Publishing Ltd. 2023
Edition:[First edition]
Subjects:
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
Description
Summary:As machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to suboptimal performance on imbalanced data. This comprehensive guide helps you address this class imbalance to significantly improve model performance. Machine Learning for Imbalanced Data begins by introducing you to the challenges posed by imbalanced datasets and the importance of addressing these issues. It then guides you through techniques that enhance the performance of classical machine learning models when using imbalanced data, including various sampling and cost-sensitive learning methods. As you progress, you’ll delve into similar and more advanced techniques for deep learning models, employing PyTorch as the primary framework. Throughout the book, hands-on examples will provide working and reproducible code that’ll demonstrate the practical implementation of each technique. By the end of this book, you’ll be adept at identifying and addressing class imbalances and confidently applying various techniques, including sampling, cost-sensitive techniques, and threshold adjustment, while using traditional machine learning or deep learning models
Physical Description:344 pages illustrations