Splitting on categorical predictors in random forests.
Categorical predictors
Classification
Random forest
Survival analysis
Journal
PeerJ
ISSN: 2167-8359
Titre abrégé: PeerJ
Pays: United States
ID NLM: 101603425
Informations de publication
Date de publication:
2019
2019
Historique:
received:
26
06
2018
accepted:
22
12
2018
entrez:
13
2
2019
pubmed:
13
2
2019
medline:
13
2
2019
Statut:
epublish
Résumé
One reason for the widespread success of random forests (RFs) is their ability to analyze most datasets without preprocessing. For example, in contrast to many other statistical methods and machine learning approaches, no recoding such as dummy coding is required to handle ordinal and nominal predictors. The standard approach for nominal predictors is to consider all 2
Identifiants
pubmed: 30746306
doi: 10.7717/peerj.6339
pii: 6339
pmc: PMC6368971
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e6339Déclaration de conflit d'intérêts
The authors declare that they have no competing interests.
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