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

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

Abstract 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 anal...

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Bibliographic Details
Main Authors: Daniel Alves de Brito Filho, Rinaldo Artes
Format: Article
Language:English
Published: Associação Brasileira de Engenharia de Produção (ABEPRO) 2018-07-01
Series:Production
Subjects:
Credit
Machine learning
Logistic regression
BART
Random Forest
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132018000100206&lng=en&tlng=en

Internet

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132018000100206&lng=en&tlng=en

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