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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Packt Publishing Ltd.
2023
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| 520 | |a 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. | ||
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| spelling | Abhishek, Kumar VerfasserIn aut Machine learning for imbalanced data tackle imbalanced datasets using machine learning and deep learning techniques Kumar Abhishek, Dr. Mounir Abdelaziz [First edition]. Birmingham, UK Packt Publishing Ltd. 2023 1 online resource (344 pages) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index 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. Machine learning Data sets Apprentissage automatique Jeux de données Abdelaziz, Mounir VerfasserIn aut TUM01 ZDB-30-ORH TUM_PDA_ORH https://learning.oreilly.com/library/view/-/9781801070836/?ar X:ORHE Aggregator lizenzpflichtig Volltext |
| spellingShingle | Abhishek, Kumar Abdelaziz, Mounir Machine learning for imbalanced data tackle imbalanced datasets using machine learning and deep learning techniques Machine learning Data sets Apprentissage automatique Jeux de données |
| title | Machine learning for imbalanced data tackle imbalanced datasets using machine learning and deep learning techniques |
| title_auth | Machine learning for imbalanced data tackle imbalanced datasets using machine learning and deep learning techniques |
| title_exact_search | Machine learning for imbalanced data tackle imbalanced datasets using machine learning and deep learning techniques |
| title_full | Machine learning for imbalanced data tackle imbalanced datasets using machine learning and deep learning techniques Kumar Abhishek, Dr. Mounir Abdelaziz |
| title_fullStr | Machine learning for imbalanced data tackle imbalanced datasets using machine learning and deep learning techniques Kumar Abhishek, Dr. Mounir Abdelaziz |
| title_full_unstemmed | Machine learning for imbalanced data tackle imbalanced datasets using machine learning and deep learning techniques Kumar Abhishek, Dr. Mounir Abdelaziz |
| title_short | Machine learning for imbalanced data |
| title_sort | machine learning for imbalanced data tackle imbalanced datasets using machine learning and deep learning techniques |
| title_sub | tackle imbalanced datasets using machine learning and deep learning techniques |
| topic | Machine learning Data sets Apprentissage automatique Jeux de données |
| topic_facet | Machine learning Data sets Apprentissage automatique Jeux de données |
| url | https://learning.oreilly.com/library/view/-/9781801070836/?ar |
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