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Recent Methods Statistics Machine by Kraus Anne (10 results)
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Recent Methods from Statistics and Machine Learning for Credit Scoring
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 25.01
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Recent Methods from Statistics and Machine Learning for Credit Scoring
Language: English
Published by Cuvillier Verlag 2014-07-08, 2014
ISBN 10: 3954047365 ISBN 13: 9783954047369
Paperback. Condition: New.
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Recent Methods from Statistics and Machine Learning for Credit Scoring
Language: English
Published by Cuvillier, Cuvillier Jul 2014, 2014
ISBN 10: 3954047365 ISBN 13: 9783954047369
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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.
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Taschenbuch. Condition: 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: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Recent Methods from Statistics and Machine Learning for Credit Scoring
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 4 out of 5 stars
£ 26.46
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketPAP. Condition: 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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Recent Methods from Statistics and Machine Learning for Credit Scoring
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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.
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Condition: 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.
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Recent Methods from Statistics and Machine Learning for Credit Scoring
Language: English
Published by Cuvillier, Cuvillier, 2014
ISBN 10: 3954047365 ISBN 13: 9783954047369
Taschenbuch. Condition: 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.
