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gaustin15 merged 2 commits into from
Mar 22, 2025
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adding pypi installation #2

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adding pypi installation
gaustin15 Mar 22, 2025
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Update README.md
gaustin15 Mar 22, 2025
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3 changes: 2 additions & 1 deletion README.md
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Expand Up @@ -10,11 +10,12 @@ This is a python package designed to facilitate correcting for distributional bi

All classes from this package provide train/test indices to split data in train/test sets while rebalancing the training set to account for distributional bias. This package is designed to enable automated rebalancing for the cross-valition implementations in formats similar to scikit-learn's `LeaveOneOut`, `StratifiedKFold`, and `LeavePOut`, through the `RebalancedCV` classes `RebalancedLeaveOneOut`, `RebalancedLeaveOneOutRegression`, `RebalancedKFold`, and `RebalancedLeavePOut`. These Rebalanced classes are designed to work in the exact same code structure and implementation use cases as their scikit-learn equivalents, with the only difference being a subsampling within the provided training indices.

For any support for using RebalancedCV, please use our <a href="https://github.com/korem-lab/RebalancedCV/issues">issues page</a> or email: gia2105@columbia.edu.

**Installation**
-------------------
```bash
pip install git+https://github.com/korem-lab/RebalancedCV.git
pip install RebalancedCV
```
The dependencies for RebalancedCV are python, numpy, and scikit-learn. Is has been developped and tested using python 3.6 - 3.12. Only standard hardware is required for RebalancedCV. The typical install time for RebalancedCV is less that 15 seconds.

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