veesa - Pipeline for Explainable Machine Learning with Functional Data
Implements the Variable importance Explainable Elastic
Shape Analysis pipeline for explainable machine learning with
functional data inputs. Converts training and testing data
functional inputs to elastic shape analysis principal
components that account for vertical and/or horizontal
variability. Computes feature importance to identify important
principal components and visualizes variability captured by
functional principal components. See Goode et al. (2025)
<doi:10.48550/arXiv.2501.07602> for technical details about the
methodology.
