2026-09-18T19:22:35Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/854462026-06-06T00:55:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Andrew W. Lo.
Mattocks, Benjamin A
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-03-06T15:42:35Z
2014-03-06T15:42:35Z
2013
2013
http://hdl.handle.net/1721.1/85446
870685388
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 49-50).
This thesis examines a support vector machine approach for determining consumer credit. The support vector machine using a radial basis function (RBF) kernel is compared to a previous implementation of a decision tree machine learning model. The dataset used for evaluation was provided by a large bank and includes relevant consumer-level data, including transactions and credit-bureau data. The results suggest that a support vector machine offers similar performance to decision trees, but the parameters specifying the soft-margin constraint and the inverse-width used in the RBF kernel could significantly affect its performance.
by Benjamin A. Mattocks.
M.Eng.
50 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
An evaluation of support vector machines in consumer credit analysis
Thesis
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