Model Selection and Evaluation

This lecture covers the importance of designing an experimental framework for selecting a supervised learning model, choosing evaluation criteria, and estimating the generalization performance. It explains the distinction between model evaluation and selection, the empirical estimation of generalization error, the significance of training and test datasets, the role of validation sets, cross-validation techniques, and the drawbacks of leave-one-out validation. The instructor emphasizes the critical aspects of model evaluation to prevent overfitting and ensure accurate performance assessment.