Metadata-Version: 2.1
Name: PFFRA
Version: 1.0.2
Summary: 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.
Home-page: https://github.com/JianqiaoMao/PFFRA
Author: Jianqiao Mao
Author-email: jxm1417@student.bham.ac.uk
License: GNU GPL-2.0
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: pywavelets
