CITATION.cff

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cff-version: 1.2.0
title: >-
  Forest-Guided Clustering - Explainability for
  Random Forest Models
message: >-
  "If you use this software, please cite it as
  below."
type: software
authors:
  - given-names: Lisa
    family-names: Barros de Andrade e Sousa
    email: lisa.barros.andrade.sousa@gmail.com
    affiliation: Helmholtz AI
    orcid: 'https://orcid.org/0000-0001-7702-9782'
  - given-names: Dominik
    family-names: Thalmeier
  - given-names: Helena
    family-names: Pelin
    email: helena.pelin@helmholtz-muenchen.de
    affiliation: Helmholtz AI
    orcid: 'https://orcid.org/0000-0001-8875-4285'
  - given-names: Marie
    family-names: Piraud
identifiers:
  - type: doi
    value: 10.5281/zenodo.6445529
repository-code: >-
  https://github.com/HelmholtzAI-Consultants-Munich/fg-clustering
url: >-
  https://forest-guided-clustering.readthedocs.io/en/latest/
abstract: >-
  This python package is about explainability of
  Random Forest models. Standard explainability
  methods (e.g. feature importance) assume
  independence of model features and hence, are not
  suited in the presence of correlated features. The
  Forest-Guided Clustering algorithm does not assume
  independence of model features, because it computes
  the feature importance based on subgroups of
  instances that follow similar decision rules within
  the Random Forest model. Hence, this method is well
  suited for cases with high correlation among model
  features.
keywords:
  - XAI
  - explainable machine learning
  - Random Forest
license: MIT
version: v0.2.0
date-released: '2022-04-11'