iutzeler/skwdro
Distributionally robust machine learning with Pytorch and Scikit-learn wrappers
This package helps data scientists and machine learning engineers build more reliable predictive models, especially when the real-world data might slightly differ from the training data. You provide your typical datasets, and it produces a robust model that is less sensitive to small changes or uncertainties in future data, improving its performance in unpredictable environments.
Available on PyPI.
Use this if you need your machine learning models to be robust and perform consistently even when there are small, unexpected shifts or uncertainties in your real-world data.
Not ideal if your data is perfectly stable and predictable, or if you need to build models that are not based on PyTorch or Scikit-learn architectures.
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19
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1
Language
Python
License
BSD-3-Clause
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
Dependencies
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