Popovych bohdan (19 resultados)

PF. Condición: New.

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Condición: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. The comparison of logistic regression, decision tree, and random forest showed that machine learning methods are able to predict credit defaults of individuals more accurately than the logit model. Furthermore, it was demonstrated how random forest and decision tree models were more sensitive in detecting default borrowers.…

Paperback. Condición: Brand New. 98 pages. 8.27x5.83x0.32 inches. In Stock.

Taschenbuch. Condición: Neu. Application of AI in Credit Scoring Modeling | Bohdan Popovych | Taschenbuch | xv | Englisch | 2022 | Springer | EAN 9783658401795 | Verantwortliche Person für die EU: Springer Gabler in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. The comparison of logistic regression, decision tree, and random forest showed that machine learning methods are able to predict credit defaults of individuals more accurately than the logit model. Furthermore, it was demonstrated how random forest and decision tree models were more sensitive in detecting default borrowers. 100 pp. Englisch.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers features. The comparison of logistic regression, decision tree, and random forest showed that machine learning me.…

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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. The comparison of logistic regression, decision tree, and random forest showed that machine learning methods are able to predict credit defaults of individuals more accurately than the logit model. Furthermore, it was demonstrated how random forest and decision tree models were more sensitive in detecting default borrowers.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 100 pp. Englisch.…

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