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Synopsis
The credit industry has grown very rapidly almost all over the world. Giving credit is regarded as a business activity which generates profits for banks and other financial institutions. However, loans can also be a source of risk of loss. The recent financial crisis has raised the attention of banks and other financial institutions about credit risk. Therefore, the Basel Committee asked all banks and financial institutions to implement a credit scoring system to assist in estimating the level of credit risk. The method for Credit Scoring has been developed to select a better model in predicting credit risk. Data mining methods are superior to the statistical methods of dealing with Credit Scoring issues, especially for nonlinear relationships between variables. By flashing the ensemble method with statistical methods, proven to achieve a higher level of accuracy than the method of data mining. the classification process will be carried out to classify prospective borrowers into two classes: good prospective borrowers and bad prospective borrowers based on the characteristics of prospective borrowers using the GradientBoost ensemble method.
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Accurately Classifying Credit Scoring using Ensemble GradientBoost
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LAP LAMBERT Academic Publishing Aug 2018, 2018
ISBN 10: 613989333X
ISBN 13: 9786139893331
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The credit industry has grown very rapidly almost all over the world. Giving credit is regarded as a business activity which generates profits for banks and other financial institutions. However, loans can also be a source of risk of loss. The recent financial crisis has raised the attention of banks and other financial institutions about credit risk. Therefore, the Basel Committee asked all banks and financial institutions to implement a credit scoring system to assist in estimating the level of credit risk. The method for Credit Scoring has been developed to select a better model in predicting credit risk. Data mining methods are superior to the statistical methods of dealing with Credit Scoring issues, especially for nonlinear relationships between variables. By flashing the ensemble method with statistical methods, proven to achieve a higher level of accuracy than the method of data mining. the classification process will be carried out to classify prospective borrowers into two classes: good prospective borrowers and bad prospective borrowers based on the characteristics of prospective borrowers using the GradientBoost ensemble method. 56 pp. Englisch. Seller Inventory # 9786139893331
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Accurately Classifying Credit Scoring using Ensemble GradientBoost
Published by
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 613989333X
ISBN 13: 9786139893331
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Accurately Classifying Credit Scoring using Ensemble GradientBoost
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LAP LAMBERT Academic Publishing, 2018
ISBN 10: 613989333X
ISBN 13: 9786139893331
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Accurately Classifying Credit Scoring using Ensemble GradientBoost
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ISBN 10: 613989333X
ISBN 13: 9786139893331
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Accurately Classifying Credit Scoring using Ensemble GradientBoost
Published by
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 613989333X
ISBN 13: 9786139893331
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The credit industry has grown very rapidly almost all over the world. Giving credit is regarded as a business activity which generates profits for banks and other financial institutions. However, loans can also be a source of risk of loss. The recent financial crisis has raised the attention of banks and other financial institutions about credit risk. Therefore, the Basel Committee asked all banks and financial institutions to implement a credit scoring system to assist in estimating the level of credit risk. The method for Credit Scoring has been developed to select a better model in predicting credit risk. Data mining methods are superior to the statistical methods of dealing with Credit Scoring issues, especially for nonlinear relationships between variables. By flashing the ensemble method with statistical methods, proven to achieve a higher level of accuracy than the method of data mining. the classification process will be carried out to classify prospective borrowers into two classes: good prospective borrowers and bad prospective borrowers based on the characteristics of prospective borrowers using the GradientBoost ensemble method. Seller Inventory # 9786139893331
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Accurately Classifying Credit Scoring using Ensemble GradientBoost
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LAP LAMBERT Academic Publishing, 2018
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ISBN 13: 9786139893331
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Accurately Classifying Credit Scoring using Ensemble GradientBoost
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Paperback. Condition: Brand New. 56 pages. 8.66x5.91x0.13 inches. In Stock. Seller Inventory # zk613989333X
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Accurately Classifying Credit Scoring using Ensemble GradientBoost
Published by
LAP LAMBERT Academic Publishing Aug 2018, 2018
ISBN 10: 613989333X
ISBN 13: 9786139893331
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The credit industry has grown very rapidly almost all over the world. Giving credit is regarded as a business activity which generates profits for banks and other financial institutions. However, loans can also be a source of risk of loss. The recent financial crisis has raised the attention of banks and other financial institutions about credit risk. Therefore, the Basel Committee asked all banks and financial institutions to implement a credit scoring system to assist in estimating the level of credit risk. The method for Credit Scoring has been developed to select a better model in predicting credit risk. Data mining methods are superior to the statistical methods of dealing with Credit Scoring issues, especially for nonlinear relationships between variables. By flashing the ensemble method with statistical methods, proven to achieve a higher level of accuracy than the method of data mining. the classification process will be carried out to classify prospective borrowers into two classes: good prospective borrowers and bad prospective borrowers based on the characteristics of prospective borrowers using the GradientBoost ensemble method.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch. Seller Inventory # 9786139893331
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Accurately Classifying Credit Scoring using Ensemble GradientBoost
Published by
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 613989333X
ISBN 13: 9786139893331
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Taschenbuch. Condition: Neu. Accurately Classifying Credit Scoring using Ensemble GradientBoost | Armin Lawi (u. a.) | Taschenbuch | 56 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139893331 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 114402726
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