Jupyter Notebook oversampling 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.
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).
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Index
| Project | Stars | |
|---|---|---|
| 1 | smote_variants | 600 |
| 2 | xrays-and-gradcam | 47 |
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