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Naïve Bayes As A Means Of Constructing Application Scorecards

In: Advances In Doctoral Research In Management

Author

Listed:
  • Anthony C. Antonakis

    (University of Piraeus, Greece)

  • Michael E. Sfakianakis

    (University of Piraeus, Greece)

Abstract

This study examines the effectiveness of the Naïve Bayes Rule relative to that of five other popular algorithms in constructing scorecards that correctly discriminate between good-risk and bad-risk credit applicants. Scorecard performance is assessed on a real-world data sample by both the percentage of correctly classified cases and the more relevant criterion of bad rate among accepts. Naive Bayes is found to produce the worst-performing scorecard under both measures used.

Suggested Citation

  • Anthony C. Antonakis & Michael E. Sfakianakis, 2008. "Naïve Bayes As A Means Of Constructing Application Scorecards," World Scientific Book Chapters, in: Luiz Moutinho & Kun-Huang Huarng (ed.), Advances In Doctoral Research In Management, chapter 3, pages 47-61, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812778666_0003
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    Keywords

    Doctoral; Research; Management Methodology; Data; Analysis; Paradigm; Modeling; International; Management Theory; Statistics; Market Survey;
    All these keywords.

    JEL classification:

    • F1 - International Economics - - Trade

    Statistics

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