Model Builder for Predictive Analytics & Fair Isaac's approach to KDD Cup 2003

  • Authors:

  • Joel Carleton;Daragh Hartnett;Joseph Milana;Michinari Momma;Joseph Sirosh;Gabriela Surpi

  • Affiliations:

  • Fair Isaac Corporation, San Diego, CA;Fair Isaac Corporation, San Diego, CA;Fair Isaac Corporation, San Diego, CA;Fair Isaac Corporation, San Diego, CA;Fair Isaac Corporation, San Diego, CA;Fair Isaac Corporation, San Diego, CA

  • Venue:
  • ACM SIGKDD Explorations Newsletter
  • Year:
  • 2003

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Abstract

Fair Isaac tackled the third task of KDD Cup 2003 using a predictive modeling approach that leveraged citation graphs, text mining, custom variable creation and linear regression. The core tools we used were embedded in our Model Builder for Predictive Analytics (MBPA) product that makes commercially available a broad set of previously proprietary methodologies used by Fair Isaac for predictive scoring systems such as credit risk and credit card fraud. This short paper reviews the KDD cup problem our approach, and the toolset. We analyze the predictive variables in the model, the main sources of prediction errors, and the steps that could be taken to alleviate such errors in future work.