Application of bayesian additive regression trees in the development of credit scoring models in Brazil

Página do item simplificado
dc.contributor.authorRINALDO ARTES
dc.contributor.authorBrito Filho, Daniel Alves de
dc.coverage.cidadeSão Paulopt_BR
dc.coverage.paisBrasilpt_BR
dc.creatorBrito Filho, Daniel Alves de
dc.date.accessioned2022-08-16T00:17:01Z
dc.date.available2022-08-16T00:17:01Z
dc.date.issued2018
dc.description.notesTexto Completopt_BR
dc.description.otherPaper aims: This paper presents a comparison of the performances of the Bayesian additive regression trees (BART), Random Forest (RF) and the logistic regression model (LRM) for the development of credit scoring models. Originality: It is not usual the use of BART methodology for the analysis of credit scoring data. The database was provided by Serasa-Experian with information regarding direct retail consumer credit operations. The use of credit bureau variables is not usual in academic papers. Research method: Several models were adjusted and their performances were compared by using regular methods. Main findings: The analysis confirms the superiority of the BART model over the LRM for the analyzed data. RF was superior to LRM only for the balanced sample. The best-adjusted BART model was superior to RF. Implications for theory and practice: The paper suggests that the use of BART or RF may bring better results for credit scoring modellingpt_BR
dc.format.extentp. 1-13pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doihttps://doi.org/10.1590/0103-6513.20170110pt_BR
dc.identifier.issn19805411pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4002
dc.identifier.volume28pt_BR
dc.language.isoInglêspt_BR
dc.publisherAssociação Brasileira de Engenharia de Produçãopt_BR
dc.relation.ispartofProductionpt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITORpt_BR
dc.subject.keywordsCreditpt_BR
dc.subject.keywordsMachine learningpt_BR
dc.subject.keywordsLogistic regressionpt_BR
dc.subject.keywordsBARTpt_BR
dc.subject.keywordsRandom Forestpt_BR
dc.titleApplication of bayesian additive regression trees in the development of credit scoring models in Brazilpt_BR
dc.typejournal article
dspace.entity.typePublication
local.subject.cnpqCiências Exatas e da Terrapt_BR
local.typeArtigo Científicopt_BR
relation.isAuthorOfPublication8b791c94-f3e5-4e04-af26-594195a8f576
relation.isAuthorOfPublication.latestForDiscovery8b791c94-f3e5-4e04-af26-594195a8f576

Arquivos

Pacote original

Agora exibindo 1 - 2 de 2
N/D
Nome:
R_Artigo_2018_Application of bayesian additive_TC.pdf
Tamanho:
786.31 KB
Formato:
Adobe Portable Document Format
Descrição:
R_Artigo_2018_Application of bayesian additive_TC
Baixar
Imagem de Miniatura
Nome:
Acesso_Primeira Pagina_Application of bayesian additive regression trees in the development of credit scoring models in Brazil.pdf
Tamanho:
486.42 KB
Formato:
Adobe Portable Document Format
Baixar

Licença do pacote

Agora exibindo 1 - 1 de 1
N/D
Nome:
license.txt
Tamanho:
282 B
Formato:
Item-specific license agreed upon to submission
Descrição:
Baixar

Coleções

Coleção de Artigos Acadêmicos