Komparativní analýza modelů pro predikci pravděpodobnosti defaultu - Vujčić, Stevan (2024)
A series of common machine learning models is estimated using behavioral home loans data of a major Czech banking institution. To address robustness, a variety of algorithms are used for modeling coupled with different approaches to data preprocessing and feature selection. Namely, several takes at feature transformations, class balancing, multicollinearity removal and feature selection are accounted for. It is found that in the case of the Czech home loans data the classical logistic regression approach is not outperformed by the more modern methods such as artificial neural networks or boosting.