Registro completo de metadatos
| Campo DC | Valor |
|---|---|
| dc.contributor.author | Alonso-Robisco, Andres |
| dc.contributor.author | Carbó, José Manuel |
| dc.date.accessioned | 2021-05-24T10:04:30Z |
| dc.date.available | 2021-05-24T10:04:30Z |
| dc.date.issued | 2021-05-24 |
| dc.identifier.uri | https://repositorio.bde.es/handle/123456789/16694 |
| dc.description | Summary of Banco de España Working Paper no. 2105 |
| dc.format.extent | 3 p. |
| dc.language.iso | en |
| dc.publisher | Banco de España |
| dc.relation.ispartof | Research Update / Banco de España, Spring 2021, p. 11-13 |
| dc.relation.hasversion | Documento relacionado 123456789/14691 |
| dc.rights | Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0) |
| dc.rights | In Copyright - Non Commercial Use Permitted |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es_ES |
| dc.rights.uri | http://rightsstatements.org/vocab/InC-NC/1.0/ |
| dc.subject | Aprendizaje automático |
| dc.subject | Riesgo de crédito |
| dc.subject | Predicción |
| dc.subject | Probabilidad de impago |
| dc.subject | Modelos IRB |
| dc.subject | Machine learning |
| dc.subject | Credit risk |
| dc.subject | Prediction |
| dc.subject | Probability of default |
| dc.subject | IRB system |
| dc.title | Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation |
| dc.type | Artículo |
| dc.identifier.bdepub | REUP-202105-3 |
| dc.subject.bde | Big data e inteligencia artificial |
| dc.subject.bde | Sistemas bancarios y actividad crediticia |
| dc.subject.bde | Métodos Econométricos y Estadísticos |
| dc.subject.bde | Modelización econométrica |
| dc.publisher.bde | Madrid : Banco de España, 2021 |
