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

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.