Explores model selection, evaluation, and generalization in machine learning, emphasizing unbiased performance estimation and the risks of over-learning.
Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.
Delves into the challenges and opportunities of machine learning in credit risk modeling, comparing traditional statistical models with machine learning methods.
