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
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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