Package: FeatureImpCluster 0.1.5

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>.

Authors:Oliver Pfaffel [aut, cre]

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FeatureImpCluster.pdf |FeatureImpCluster.html✨
FeatureImpCluster/json (API)
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Bug tracker:https://github.com/o1iv3r/featureimpcluster/issues

On CRAN:

3 exports 4 stars 1.27 score 29 dependencies 1 mentions 17 scripts 330 downloads

Last updated 3 years agofrom:f66ba1c064. Checks:OK: 7. Indexed: yes.

Exports:create_random_dataFeatureImpClusterPermMisClassRate

Dependencies:clicolorspacedata.tablefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr