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

pip install rfmsu

Example

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.