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

Summary/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 – consumer acceptance during a direct marketing campaign, predicting default on credit card debt, credit scoring, forecasting firm insolvency, and modeling 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 modeling a wide range of discrete choice situations.

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