Enhancing Support for Imbalanced Datasets #27361
davitacols
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I gave a talk this year at EuroSciPy: https://www.youtube.com/watch?v=6YnhoCfArQo&ab_channel=EuroSciPy In this presentation, I tackle the problem of imbalance classification with experience that I could get by developing imbalanced-learn and thoughts that we got with people within the scikit-learn community. In short, there is no real problem of learning from imbalanced dataset. If you still want to experiment with some of the techniques that you mentioned, you can have a look at |
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Scikit-learn is a powerful library for machine learning, but effectively handling imbalanced datasets remains a challenge in many real-world scenarios. Imbalanced datasets, where one class significantly outnumbers the others, are common in areas such as fraud detection, medical diagnosis, and anomaly detection.
Proposal
I would like to propose an enhancement to scikit-learn's capabilities to better support imbalanced datasets. This improvement encompasses several aspects:
Algorithmic Enhancements
We can explore opportunities to improve existing algorithms for imbalanced datasets or introduce new specialized algorithms.
Sampling Strategies
Implement and document various sampling strategies, including oversampling, undersampling, and hybrid techniques, to address class imbalance effectively.
Evaluation Metrics
We should provide a comprehensive set of evaluation metrics that are specifically designed for imbalanced datasets. These metrics go beyond accuracy and include precision, recall, F1-score, area under the precision-recall curve, etc.
Documentation and Tutorials
Create detailed documentation and tutorials that guide users on handling imbalanced datasets using scikit-learn. Real-world examples and best practices will be included.
Integration with Pipelines
Ensure that imbalanced data handling techniques can be seamlessly integrated into scikit-learn's pipeline architecture for end-to-end machine learning workflows.
Benefits
Improved model performance on imbalanced datasets.
Greater accessibility and ease of use for handling imbalanced data within the scikit-learn ecosystem.
Enhanced documentation and educational resources for users dealing with imbalanced datasets.
Community Involvement:
I invite the scikit-learn community to participate in this discussion. Your input, feedback, and potential use cases related to imbalanced datasets are valuable. Let's work together to make scikit-learn even more powerful for real-world applications.
Please feel free to share your thoughts, ideas, and suggestions regarding this proposed enhancement. Your contributions to this discussion will help shape the future direction of scikit-learn's support for imbalanced datasets.
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