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Synopsis
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.
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Recent Methods from Statistics and Machine Learning for Credit Scoring
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Recent Methods from Statistics and Machine Learning for Credit Scoring
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Recent Methods from Statistics and Machine Learning for Credit Scoring
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Recent Methods from Statistics and Machine Learning for Credit Scoring
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Cuvillier Verlag, 2014
ISBN 10 : 3954047365
ISBN 13 : 9783954047369
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Kartoniert / Broschiert. Etat : 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. N° de réf. du vendeur 74490250
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Recent Methods from Statistics and Machine Learning for Credit Scoring
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Recent Methods from Statistics and Machine Learning for Credit Scoring
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783954047369
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Recent Methods from Statistics and Machine Learning for Credit Scoring
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783954047369
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Recent Methods from Statistics and Machine Learning for Credit Scoring (Paperback or Softback)
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Cuvillier 7/8/2014, 2014
ISBN 10 : 3954047365
ISBN 13 : 9783954047369
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Paperback or Softback. Etat : New. Recent Methods from Statistics and Machine Learning for Credit Scoring 0.45. Book. N° de réf. du vendeur BBS-9783954047369
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Recent Methods from Statistics and Machine Learning for Credit Scoring
Edité par
Cuvillier, Cuvillier Jul 2014, 2014
ISBN 10 : 3954047365
ISBN 13 : 9783954047369
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783954047369
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