An evaluation of support vector machines in consumer credit analysis
Name
870685388-MIT.pdf
Description
Full printable version
Size
2.42 MB
Format
Adobe PDF
Checksum (MD5)
0b861193cfff9795d45416e0b5d823b0
Author(s)
Mattocks, Benjamin A
Advisor(s)
Andrew W. Lo.
Date Issued
2013
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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