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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Production
Hlavní autoři: Daniel Alves de Brito, Rinaldo Artes
Médium: Artigo
Jazyk:Inglês
Vydáno: Associação Brasileira de Engenharia de Produção 2018
Témata:
Ingeniería
BART
Credit
Random Forest
Machine learning
Logistic regression
On-line přístup:https://www.redalyc.org/articulo.oa?id=396754754010
https://www.redalyc.org/journal/3967/396754754010/
https://www.redalyc.org/journal/3967/396754754010/html/
https://www.redalyc.org/journal/3967/396754754010/396754754010.epub
https://www.redalyc.org/journal/3967/396754754010/movil
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo otaguje tento záznam!

Podobné jednotky