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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.5 |
rolling source/ R- | FeatureImpCluster_0.1.5.tar.gz |
45.6 KiB |
0.1.5 |
latest source/ R- | FeatureImpCluster_0.1.5.tar.gz |
45.6 KiB |
0.1.5 |
2026-04-23 source/ R- | FeatureImpCluster_0.1.5.tar.gz |
45.6 KiB |
0.1.5 |
2026-04-09 windows/windows R-4.5 | FeatureImpCluster_0.1.5.zip |
62.7 KiB |