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

Paper 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 cre...

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Bibliografiske detaljer
Udgivet i:Production
Main Authors: Daniel Alves de Brito, Rinaldo Artes
Format: Artigo
Sprog:Inglês
Udgivet: Associação Brasileira de Engenharia de Produção 2018
Fag:
Ingeniería
BART
Credit
Random Forest
Machine learning
Logistic regression
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