FACT
Feature Attributions for ClusTering
We present 'FACT' (Feature Attributions for ClusTering), a framework for unsupervised interpretation methods that can be used with an arbitrary clustering algorithm. The package is capable of re-assigning instances to clusters (algorithm agnostic), preserves the integrity of the data and does not introduce additional models. 'FACT' is inspired by the principles of model-agnostic interpretation in supervised learning. Therefore, some of the methods presented are based on 'iml', a R Package for Interpretable Machine Learning by Christoph Molnar, Giuseppe Casalicchio, and Bernd Bischl (2018)
- Version0.1.1
- R versionunknown
- LicenseLGPL-3
- Needs compilation?No
- Languageen-US
- Last release03/25/2024
Documentation
Team
Henri Funk
Christian Scholbeck
Show author detailsRolesAuthor, ContributorGiuseppe Casalicchio
Show author detailsRolesAuthor, Contributor
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- Imports6 packages
- Suggests11 packages