Nonstandard Errors in Bank Default Prediction Using Machine Learning
This thesis analyses the risk of nonstandard errors affecting bank prediction using machine learning. Nonstandard errors are defined as the type of errors that occur during the Evidence Generating Process (EGP), meaning that these occur as a consequence of decision-making by researchers, rather than from sampling. The aim is to analyze how different choices of methods for pre-processing and data e