pyFOCI - Feature Ordering by Conditional Independence
pyFOCI provides the feature selection algorithm "Feature Ordering by Conditional Independence" (FOCI), based on a nonlinear generalization of the partial R² statistic. So it can be especially useful in strongly nonlinear data scenarios.
It is based on
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Mona Azadkia and Sourav Chatterjee. A simple measure of conditional dependence. The Annals of Statistics, 49(6):3070–3102, 2021. [DOI] [arXiv]
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Sebastian Fuchs. Quantifying directed dependence via dimension reduction. Journal of Multivariate Analysis 201 (2024): 105266. [DOI] [arXiv]
The Package is scikit-learn compatible. It is available on PyPI.
Refer to the documentation (API and example code) at https://m3dm-jku.github.io/pyFOCI/ .
This work has been supported by the COMET-K2 Center of the Linz Center of Mechatronics (LCM), funded by the Austrian federal government and the federal state of Upper Austria.
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Release files for pyFOCI 0.9.0
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