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Binary Classification Problems in Economics and 136 Different Ways to Solve Them

Author

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  • Anton Gerunov

    (Faculty of Economics and Business Administration, Sofia University ÒSt. Kliment Ohridski")

Abstract

This article investigates the performance of 136 different classification algorithms for economic problems of binary choice. They are applied to model five different choice situations Ð consumer acceptance during a direct marketing campaign, predicting default on credit card debt, credit scoring, forecasting firm insolvency, and modelling online consumer purchases. Algorithms are trained to generate class predictions of a given binary target variable, which are then used to measure their forecast accuracy using the area under a ROC curve. Results show that algorithms of the Random Forest family consistently outperform alternative methods and may be thus suitable for modelling a wide range of discrete choice situations.

Suggested Citation

  • Anton Gerunov, 2020. "Binary Classification Problems in Economics and 136 Different Ways to Solve Them," Bulgarian Economic Papers bep-2020-02, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria // Center for Economic Theories and Policies at Sofia University St Kliment Ohridski, revised Mar 2020.
    • Handle: RePEc:sko:wpaper:bep-2020-02
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    File URL: https://www.uni-sofia.bg/index.php/eng/content/download/230101/1534214/file/BEP-2020-02.pdf
    File Function: First version, 2020
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    Citations

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    Cited by:

    1. Anton Gerunov, 2023. "Modern Approaches To Forecasting Firm Default Rates Over The Short To Medium Term: An Application To A Panel Of Polish Companies," Yearbook of the Faculty of Economics and Business Administration, Sofia University, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria, vol. 22(1), pages 5-15, October.

    More about this item

    Keywords

    Bdiscrete choice; classification; machine learning algorithms; modelling decisions.;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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