Kumar Abhishek & Dr. Mounir Abdelaziz 
Machine Learning for Imbalanced Data [EPUB ebook] 
Tackle imbalanced datasets using machine learning and deep learning techniques

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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 Py Torch 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.

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Language English ● Format EPUB ● Pages 344 ● ISBN 9781801070881 ● File size 16.1 MB ● Publisher Packt Publishing ● Published 2023 ● Downloadable 24 months ● Currency EUR ● ID 9265880 ● Copy protection Adobe DRM
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