ZhiningLiu1998/awesome-imbalanced-learning
😎 Everything about class-imbalanced/long-tail learning: papers, codes, frameworks, and libraries | 有关类别不平衡/长尾学习的一切：论文、代码、框架与库
This project helps data scientists and machine learning engineers address the common 'class imbalance' problem, where some categories in a dataset have significantly fewer examples than others. It provides a curated collection of research papers, code implementations, and software libraries designed to improve the accuracy of classification models built with imbalanced data, ultimately leading to better predictive performance for rare events.
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Use this if your classification models struggle to accurately predict rare events or minority classes because your training data contains a disproportionate number of examples for common classes.
Not ideal if you are looking for a complete, out-of-the-box solution to implement, rather than a curated list of resources to research and build your own solution.
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Feb 25, 2025
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