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An Interpretable Machine Learning technique to analyse the contribution of features in the frequency domain. This method is inspired by permutation feature importance analysis but aims to quantify and analyse the time-series predictive model's mechanism from a global perspective.
pip install pffra
PyPI declares 3 unique dependency rules for this release. Environment markers are shown when supplied by the project.
PFFRA publishes 1 wheel and 1 source archive for version 1.0.2. Wheel platform tags: any.
No version-specific Python classifiers are declared.
PyPI does not currently declare: Python requirement. PyDeps marks these fields as unknown instead of guessing values.
PyPI lists 3 releases with files. The first dated release is ; 2 releases fall within the 365 days preceding the latest dated release. The current release files were uploaded on . The preceding dated release was .