Delves into the challenges and opportunities of machine learning in credit risk modeling, comparing traditional statistical models with machine learning methods.
Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.
Explores predictive consistency in sequential forecasting systems, emphasizing the utility of prediction over estimation and the significance of prequential approaches.