Random forest similarity maps: a scalable visual representation for global and local interpretation (2021)
- Authors:
- Autor USP: POPOLIN NETO, MÁRIO - ICMC
- Unidade: ICMC
- DOI: 10.3390/electronics10222862
- Subjects: VISUALIZAÇÃO; APRENDIZADO COMPUTACIONAL
- Keywords: Random Forest; classification model visualization; explainable artificial intelligence (XAI); dimensionality reduction
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Electronics
- ISSN: 2079-9292
- Volume/Número/Paginação/Ano: v. 10, p. 1-20, 2021
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
MAZUMDAR, Dipankar e POPOLIN NETO, Mário e PAULOVICH, Fernando Vieira. Random forest similarity maps: a scalable visual representation for global and local interpretation. Electronics, v. 10, p. 1-20, 2021Tradução . . Disponível em: https://doi.org/10.3390/electronics10222862. Acesso em: 05 jun. 2024. -
APA
Mazumdar, D., Popolin Neto, M., & Paulovich, F. V. (2021). Random forest similarity maps: a scalable visual representation for global and local interpretation. Electronics, 10, 1-20. doi:10.3390/electronics10222862 -
NLM
Mazumdar D, Popolin Neto M, Paulovich FV. Random forest similarity maps: a scalable visual representation for global and local interpretation [Internet]. Electronics. 2021 ; 10 1-20.[citado 2024 jun. 05 ] Available from: https://doi.org/10.3390/electronics10222862 -
Vancouver
Mazumdar D, Popolin Neto M, Paulovich FV. Random forest similarity maps: a scalable visual representation for global and local interpretation [Internet]. Electronics. 2021 ; 10 1-20.[citado 2024 jun. 05 ] Available from: https://doi.org/10.3390/electronics10222862 - Random Forest interpretability - explaining classification models and multivariate data through logic rules visualizations
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Informações sobre o DOI: 10.3390/electronics10222862 (Fonte: oaDOI API)
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