Metadata-Version: 2.4
Name: rfmsu
Version: 0.1.2
Summary: Weka-inspired Random Forest with Multivariate Symmetrical Uncertainty
Author: Miguel Garcia Torres, Thomas Vanhaeren
License-Expression: MIT
Project-URL: Repository, https://github.com/mgarciat/rf-msu
Project-URL: Documentation, https://github.com/mgarciat/rf-msu/tree/main/manual
Project-URL: Scientific Paper, https://doi.org/10.1016/j.patcog.2025.111939
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.23
Requires-Dist: pandas>=1.5
Requires-Dist: joblib>=1.2
Provides-Extra: fast
Requires-Dist: numba>=0.57; extra == "fast"
Requires-Dist: polars>=2.0; extra == "fast"

# RFMSU

Random Tree and Random Forest classifiers based on
Multivariate Symmetrical Uncertainty (MSU).

## Features

- Random Tree classifier.
- Random Tree with MSU.
- Random Forest classifier.
- Random Forest with MSU.
- Local and global discretization.
- Parallel training using Joblib.
- Out-of-bag evaluation.

## Installation

```bash
pip install rfmsu
```

## Example

```python
from rfmsu import RandomForestMSU

clf = RandomForestMSU(
    numIterations=100,
    rediscretize_msu_per_node=True,
    n_jobs=-1
)

clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
```


## References

RFMSU implements Random Tree and Random Forest
classifiers incorporating Multivariate Symmetrical
Uncertainty (MSU).

For further details about RFmsu and its theoretical
foundations, see:

García-Torres, M., Saucedo, F., Divina, F., & Gómez-Guerrero, S. (2026).
RFMSU: A multivariate symmetrical uncertainty-based random forest.
Pattern Recognition, 169, 111939.
DOI: https://doi.org/10.1016/j.patcog.2025.111939

## License

MIT License.
