Credit Risk Analytics: Predictive Modeling Techniques Comparison: Automated comparison of various predictive modeling techniques on credit card data - Brossura
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Credit Scoring studies are very important for any financial house. Both traditional statistical and modern data mining/machine learning tools have been evaluated in the credit scoring problem. Predictive modeling defaulter risk is one of the important problems in credit risk management. There are quite a few aggregate models and data driven models available in literature But very few of the studies facilitate the comparison of majority of the commonly employed tools in single comprehensive study. Additionally no study assesses the performance on more then two data sets and reports the results at the same time. So a macro or a simulator is designed which would work on multiple data sets and make the process of credit scoring transparent to the novice user. In initial stage, tools were compared using Dtreg predictive modeling software. Subsequently a SAS macro is developed to evaluate the effectiveness of tools available in SAS enterprise miner. The results revealed that support vector machine and genetic programming are superior tools for the purpose of classifying the loan applicant as their misclassification rates were least as compared to others. Also cross validation is essentia
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L'autore
Ravinder Singh, ME(Master of Engineering), Data Analyst/Predictive Modeler/SAS consultant at Knowledge Business solutions,Bangalore, SAS advance certified
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Credit Risk Analytics: Predictive Modeling Techniques Comparison
Editore:
LAP LAMBERT Academic Publishing Dez 2012, 2012
ISBN 10: 3659300861
ISBN 13: 9783659300868
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Credit Scoring studies are very important for any financial house. Both traditional statistical and modern data mining/machine learning tools have been evaluated in the credit scoring problem. Predictive modeling defaulter risk is one of the important problems in credit risk management. There are quite a few aggregate models and data driven models available in literature But very few of the studies facilitate the comparison of majority of the commonly employed tools in single comprehensive study. Additionally no study assesses the performance on more then two data sets and reports the results at the same time. So a macro or a simulator is designed which would work on multiple data sets and make the process of credit scoring transparent to the novice user. In initial stage, tools were compared using Dtreg predictive modeling software. Subsequently a SAS macro is developed to evaluate the effectiveness of tools available in SAS enterprise miner. The results revealed that support vector machine and genetic programming are superior tools for the purpose of classifying the loan applicant as their misclassification rates were least as compared to others. Also cross validation is essentia 156 pp. Englisch. Codice articolo 9783659300868
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Credit Risk Analytics: Predictive Modeling Techniques Comparison
Editore:
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659300861
ISBN 13: 9783659300868
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Singh RavinderRavinder Singh, ME(Master of Engineering), Data Analyst/Predictive Modeler/SAS consultant at Knowledge Business solutions,Bangalore, SAS advance certifiedCredit Scoring studies are very important for any financial h. Codice articolo 5146864
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Credit Risk Analytics: Predictive Modeling Techniques Comparison
Editore:
LAP LAMBERT Academic Publishing Dez 2012, 2012
ISBN 10: 3659300861
ISBN 13: 9783659300868
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Credit Scoring studies are very important for any financial house. Both traditional statistical and modern data mining/machine learning tools have been evaluated in the credit scoring problem. Predictive modeling defaulter risk is one of the important problems in credit risk management. There are quite a few aggregate models and data driven models available in literature But very few of the studies facilitate the comparison of majority of the commonly employed tools in single comprehensive study. Additionally no study assesses the performance on more then two data sets and reports the results at the same time. So a macro or a simulator is designed which would work on multiple data sets and make the process of credit scoring transparent to the novice user. In initial stage, tools were compared using Dtreg predictive modeling software. Subsequently a SAS macro is developed to evaluate the effectiveness of tools available in SAS enterprise miner. The results revealed that support vector machine and genetic programming are superior tools for the purpose of classifying the loan applicant as their misclassification rates were least as compared to others. Also cross validation is essentiaVDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 156 pp. Englisch. Codice articolo 9783659300868
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Credit Risk Analytics: Predictive Modeling Techniques Comparison : Automated comparison of various predictive modeling techniques on credit card data
Editore:
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659300861
ISBN 13: 9783659300868
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Credit Scoring studies are very important for any financial house. Both traditional statistical and modern data mining/machine learning tools have been evaluated in the credit scoring problem. Predictive modeling defaulter risk is one of the important problems in credit risk management. There are quite a few aggregate models and data driven models available in literature But very few of the studies facilitate the comparison of majority of the commonly employed tools in single comprehensive study. Additionally no study assesses the performance on more then two data sets and reports the results at the same time. So a macro or a simulator is designed which would work on multiple data sets and make the process of credit scoring transparent to the novice user. In initial stage, tools were compared using Dtreg predictive modeling software. Subsequently a SAS macro is developed to evaluate the effectiveness of tools available in SAS enterprise miner. The results revealed that support vector machine and genetic programming are superior tools for the purpose of classifying the loan applicant as their misclassification rates were least as compared to others. Also cross validation is essentia. Codice articolo 9783659300868
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Credit Risk Analytics: Predictive Modeling Techniques Comparison | Automated comparison of various predictive modeling techniques on credit card data
Editore:
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659300861
ISBN 13: 9783659300868
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Da: preigu, Osnabrück, Germania
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Taschenbuch. Condizione: Neu. Credit Risk Analytics: Predictive Modeling Techniques Comparison | Automated comparison of various predictive modeling techniques on credit card data | Ravinder Singh | Taschenbuch | 156 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659300868 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 106088207
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