  This study conducts a benchmarking study, comparing 23 different statistical
and machine learning methods in a credit scoring application. In order to do
so, the models' performance is evaluated over four different data sets in
combination with five data sampling strategies to tackle existing class
imbalances in the data. Six different performance measures are used to cover
different aspects of predictive performance. The results indicate a strong
superiority of ensemble methods and show that simple sampling strategies
deliver better results than more sophisticated ones.
