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Machine Learning Classification Model Comparison

Author

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  • Giudici, Paolo
  • Gramegna, Alex
  • Raffinetti, Emanuela

Abstract

Machine learning models are boosting Artificial Intelligence applications in many domains, such as automotive, finance and health care. This is mainly due to their advantage, in terms of predictive accuracy, with respect to classic statistical models. However, machine learning models are much less explainable: less transparent, less interpretable. This paper proposes to improve machine learning models, by proposing a model selection methodology, based on Lorenz Zonoids, which allows to compare them in terms of predictive accuracy significant gains, leading to a selected model which maintains accuracy while improving explainability. We illustrate our proposal by means of simulated datasets and of a real credit scoring problem. The analysis of the former shows that the proposal improves alternative methods, based on the AUROC. The analysis of the latter shows that the proposal leads to models made up of two/three relevant variables that measure the profitability and the financial leverage of the companies asking for credit.

Suggested Citation

  • Giudici, Paolo & Gramegna, Alex & Raffinetti, Emanuela, 2023. "Machine Learning Classification Model Comparison," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    • Handle: RePEc:eee:soceps:v:87:y:2023:i:pb:s0038012123000605
    DOI: 10.1016/j.seps.2023.101560
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    References listed on IDEAS

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    1. Paolo Giudici & Emanuela Raffinetti, 2020. "Lorenz Model Selection," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 754-768, October.
    2. Bracke, Philippe & Datta, Anupam & Jung, Carsten & Sen, Shayak, 2019. "Machine learning explainability in finance: an application to default risk analysis," Bank of England working papers 816, Bank of England.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    5. Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
    6. Lerman, Robert I. & Yitzhaki, Shlomo, 1984. "A note on the calculation and interpretation of the Gini index," Economics Letters, Elsevier, vol. 15(3-4), pages 363-368.
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    Cited by:

    1. Lu, Hao & Fan, Yiwei & Jiao, Liudan & Wu, Ya, 2024. "Assessment and spatial effect of urban agglomeration business environments: A case study of two urban agglomerations in China," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).

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