Metadata-Version: 2.4
Name: rfmsu
Version: 0.1.1
Summary: Weka-inspired Random Forest with Multivariate Symmetrical Uncertainty
Author: Miguel Garcia Torres, Thomas Vanhaeren
License-Expression: MIT
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; 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)
```

## License

MIT License.
