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

Anton Gerunov ()

Bulgarian Economic Papers from Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria // Center for Economic Theories and Policies at Sofia University St 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.

Keywords: Bdiscrete choice; classification; machine learning algorithms; modelling decisions. (search for similar items in EconPapers)
JEL-codes: C35 C44 C45 D81 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2020-03, Revised 2020-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dcm, nep-ecm, nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:sko:wpaper:bep-2020-02

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