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Recent Methods from Statistics and Machine Learning for Credit Scoring. N° de ref. del artículo BBS-9783954047369
Credit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of the models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them credit. The area under the receiver operating characteristic (ROC) curve (AUC) is one of the most commonly used measures to evaluate predictive performance in credit scoring. The aim of this thesis is to benchmark different methods for building scoring models in order to maximize the AUC. While this measure is used to evaluate the predictive accuracy of the presented algorithms, the AUC is especially introduced as direct optimization criterion.
Título: Recent Methods from Statistics and Machine ...
Editorial: Cuvillier 7/8/2014
Año de publicación: 2014
Encuadernación: Paperback or Softback
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Taschenbuch. Condición: Neu. Neuware -Credit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of the models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them credit. The area under the receiver operating characteristic (ROC) curve (AUC) is one of the most commonly used measures to evaluate predictive performance in credit scoring.The aim of this thesis is to benchmark different methods for building scoring models in order to maximize the AUC. While this measure is used to evaluate the predictive accuracy of the presented algorithms, the AUC is especially introduced as direct optimization criterion.Books on Demand GmbH, Überseering 33, 22297 Hamburg 166 pp. Englisch. Nº de ref. del artículo: 9783954047369
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Credit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of the models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them credit. The area under the receiver operating characteristic (ROC) curve (AUC) is one of the most commonly used measures to evaluate predictive performance in credit scoring.The aim of this thesis is to benchmark different methods for building scoring models in order to maximize the AUC. While this measure is used to evaluate the predictive accuracy of the presented algorithms, the AUC is especially introduced as direct optimization criterion. 166 pp. Englisch. Nº de ref. del artículo: 9783954047369
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Credit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of the models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them credit. The area under the receiver operating characteristic (ROC) curve (AUC) is one of the most commonly used measures to evaluate predictive performance in credit scoring.The aim of this thesis is to benchmark different methods for building scoring models in order to maximize the AUC. While this measure is used to evaluate the predictive accuracy of the presented algorithms, the AUC is especially introduced as direct optimization criterion. Nº de ref. del artículo: 9783954047369
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextrnrnCredit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of the models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them. Nº de ref. del artículo: 74490250
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Taschenbuch. Condición: Neu. Recent Methods from Statistics and Machine Learning for Credit Scoring | Anne Kraus | Taschenbuch | 166 S. | Englisch | 2014 | Cuvillier | EAN 9783954047369 | Verantwortliche Person für die EU: Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen, info[at]cuvillier[dot]de | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 104319429
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