FeatureImpCluster: Feature Importance for Partitional Clustering

Implements a novel approach for measuring feature importance in k-means clustering. Importance of a feature is measured by the misclassification rate relative to the baseline cluster assignment due to a random permutation of feature values. An explanation of permutation feature importance in general can be found here: <https://christophm.github.io/interpretable-ml-book/feature-importance.html>.

Getting started

Package details

AuthorOliver Pfaffel [aut, cre]
MaintainerOliver Pfaffel <opfaffel@gmail.com>
Package repositoryView on CRAN
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FeatureImpCluster documentation built on Oct. 20, 2021, 5:06 p.m.