| Summary: | 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.
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