Top 3 Jupyter Notebook imbalanced-data Projects
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smote_variants
A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features
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xrays-and-gradcam
Classification and Gradient-based Localization of Chest Radiographs using PyTorch.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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radius-constrained-kmeans
Codes for "No More Than 6FT Apart: Robust K-Means via Radius Upper Bounds", ICASSP 2022
Project mention: HIGHLY unbalanced dataset (>600:1 negative:positive examples), how do I deal with this? | /r/learnmachinelearning | 2023-06-06You can try data augmentation approaches (e.g., smote-variants) or synthetic data generation (e.g., ydata-synthetic). Based on the ratio, I would also try learning the characteristics of you majority class and then generate a smaller sample for it (undersampling).
Index
What are some of the best open-source imbalanced-data projects in Jupyter Notebook? This list will help you:
Project | Stars | |
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1 | smote_variants | 600 |
2 | xrays-and-gradcam | 47 |
3 | radius-constrained-kmeans | 2 |
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