CRFE 0.1.1

Conformal Recursive Feature Elimination

CRFE - Conformal Recursive Feature Selection

CRFE is a feature selection method based on recursive backward elimination, grounded in the Conformal Prediction framework [1]. The method minimizes feature non-conformity [2] through an iterative elimination policy and incorporates an automatic stopping criterion for recursive procedures.

Requirements

• Python 3.9+
• scikit-learn 1.2.2+
• numpy

Quickstart

Install from PyPI with:

pip install CRFE

or use it with pixi (installs CRFE from PyPI via the provided pixi.toml):

pixi install
pixi run check-crfe

Examples

For complete usage scripts, see:

• Library/Examples/example_1.py (multiclass synthetic benchmark with noisy variables)
• Library/Examples/example_2.py (Iris dataset with synthetic noise and downstream evaluation)

Both examples use the public package interface:

from CRFE import CRFE, ParamParada

Folder layout

CRFE/
├── Library/
│   ├── CRFE/
│   │   ├── _crfe.py
│   │   ├── _crfe_utils.py
│   │   ├── documentation.txt
│   │   └── stopping.py
│   └── Examples/
│       ├── example_1.py
│       └── example_2.py
├── LICENSE
└── readme.md

References

[1] V. Balasubramanian, S.-S. Ho, and V. Vovk, Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications, 1st ed. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2014.

[2] T. Bellotti, Z. Luo, and A. Gammerman, “Strangeness Minimisation Feature Selection with Confidence Machines,” in Intelligent Data Engineering and Automated Learning IDEAL 2006, ser. Lecture Notes in Computer Science, E. Corchado, H. Yin, V. Botti, and C. Fyfe, Eds. Berlin, Heidelberg: Springer, pp. 978–985, 2006