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Idiot's Bayes—Not So Stupid After All?

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  • David J. Hand
  • Keming Yu

Abstract

Folklore has it that a very simple supervised classification rule, based on the typically false assumption that the predictor variables are independent, can be highly effective, and often more effective than sophisticated rules. We examine the evidence for this, both empirical, as observed in real data applications, and theoretical, summarising explanations for why this simple rule might be effective. La tradition veunt qu'une règle très simple assumant l'independance des variables prédictives. une hypothèse fausse dans la plupart des cas, peut être très efficace, souvent même plus efficace qu'une méthode plus sophistiquée en ce qui concerne l'attribution de classes a un groupe d'objets. A ce sujet, nous examinons les preuves empiriques, et les preuves théoriques, e'est‐a‐dire les raisons pour lesquelles cette simple règle pourrait faciliter le processus de tri.

Suggested Citation

  • David J. Hand & Keming Yu, 2001. "Idiot's Bayes—Not So Stupid After All?," International Statistical Review, International Statistical Institute, vol. 69(3), pages 385-398, December.
    • Handle: RePEc:bla:istatr:v:69:y:2001:i:3:p:385-398
    DOI: 10.1111/j.1751-5823.2001.tb00465.x
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    Cited by:

    1. Sascha O. Becker & Luigi Pascali, 2019. "Religion, Division of Labor, and Conflict: Anti-semitism in Germany over 600 Years," American Economic Review, American Economic Association, vol. 109(5), pages 1764-1804, May.
    2. Rajeev D S Raizada & Yune-Sang Lee, 2013. "Smoothness without Smoothing: Why Gaussian Naive Bayes Is Not Naive for Multi-Subject Searchlight Studies," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-10, July.
    3. Becker, Sascha O. & Pascali, Luigi, 2016. "Religion, Division of Labor and Conflict: Anti-Semitism in German Regions over 600 Years," CAGE Online Working Paper Series 288, Competitive Advantage in the Global Economy (CAGE).
    4. Aletti, Giacomo, 2018. "Generation of discrete random variables in scalable frameworks," Statistics & Probability Letters, Elsevier, vol. 132(C), pages 99-106.
    5. Gediminas Adomavicius & Yaqiong Wang, 2022. "Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 503-521, January.
    6. Brighton, Henry, 2020. "Statistical foundations of ecological rationality," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-32.
    7. DE CNUDDE, Sofie & MARTENS, David & EVGENIOU, Theodoros & PROVOST, Foster, 2017. "A benchmarking study of classification techniques for behavioral data," Working Papers 2017005, University of Antwerp, Faculty of Business and Economics.
    8. Marvin, Hans J.P. & Bouzembrak, Yamine, 2020. "A system approach towards prediction of food safety hazards: Impact of climate and agrichemical use on the occurrence of food safety hazards," Agricultural Systems, Elsevier, vol. 178(C).
    9. Marbac, Matthieu & Vandewalle, Vincent, 2019. "A tractable multi-partitions clustering," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 167-179.

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