How do sampling strategies influence the accuracy and interpretability of bankruptcy predictions in U.S. firms?
This thesis investigates how sampling strategies influence the predictive accuracy and interpretability of bankruptcy models. Using financial data from U.S. publicly listed firms (2009 to 2023), it compares logistic regression and random forest classifiers trained under three sampling setups: no resampling, random undersampling (RUS), and SMOTE. Models are trained to predict one-year-ahead bankrup
