artefactory/mgs-grf

MGS-GRF for imbalanced-mixed-tabular data (AISTATS 2026 and ECML-PKDD 2025)

46
/ 100
Emerging

This tool helps data scientists and machine learning engineers prepare datasets where one outcome is much rarer than others, like identifying fraud or rare diseases. It takes your raw, imbalanced dataset, which can contain both numbers and categories, and generates synthetic data to balance the classes. The output is a larger, balanced dataset ready for training more accurate predictive models.

Available on PyPI.

Use this if you are building machine learning models on datasets where certain outcomes are very rare, and your models struggle to predict these infrequent events accurately.

Not ideal if your dataset is already well-balanced across all target classes or if you are not working with mixed-type (numeric and categorical) tabular data.

fraud-detection customer-churn predictive-modeling risk-assessment medical-diagnosis
Maintenance 10 / 25
Adoption 8 / 25
Maturity 25 / 25
Community 3 / 25

How are scores calculated?

Stars

49

Forks

1

Language

Python

License

MIT

Last pushed

Mar 06, 2026

Commits (30d)

0

Dependencies

3

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