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>.


Reference manual

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0.1.5 by Oliver Pfaffel, 7 days ago

Browse source code at https://github.com/cran/FeatureImpCluster

Authors: Oliver Pfaffel [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports ggplot2

Depends on data.table

Suggests flexclust, clustMixType, knitr, rmarkdown, testthat, attempt, ClustImpute, covr

See at CRAN