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Handling Correlations in Random Forests: which Impacts on Variable Importance and Model Interpretability?

hal.structure.identifierMéthodes avancées d’apprentissage statistique et de contrôle [ASTRAL]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierSafran Aircraft Engines
dc.contributor.authorLACAILLE, Jerome
hal.structure.identifierStatistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
hal.structure.identifierSafran Aircraft Engines
dc.contributor.authorMOURER, Alex
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorOLTEANU, Madalina
dc.date.accessioned2024-04-04T02:43:23Z
dc.date.available2024-04-04T02:43:23Z
dc.date.conference2021-10-06
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191330
dc.description.abstractEnThe present manuscript tackles the issues of model interpretability and variable importance in random forests, in the presence of correlated input variables. Variable importance criteria based on random permutations are known to be sensitive when input variables are correlated, and may lead for instance to unreliability in the importance ranking. In order to overcome some of the problems raised by correlation, an original variable importance measure is introduced. The proposed measure builds upon an algorithm which clusters the input variables based on their correlations, and summarises each such cluster by a synthetic variable. The effectiveness of the proposed criterion is illustrated through simulations in a regression context, and compared with several existing variable importance measures.
dc.language.isoen
dc.title.enHandling Correlations in Random Forests: which Impacts on Variable Importance and Model Interpretability?
dc.typeCommunication dans un congrès
dc.subject.halMathématiques [math]/Statistiques [math.ST]
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
bordeaux.countryBE
bordeaux.conference.cityBruges
bordeaux.peerReviewedoui
hal.identifierhal-03483385
hal.version1
hal.invitednon
hal.proceedingsnon
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03483385v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=CHAVENT,%20Marie&LACAILLE,%20Jerome&MOURER,%20Alex&OLTEANU,%20Madalina&rft.genre=unknown


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