Model Selection: Evaluation and Generalization

This lecture covers the importance of model selection in machine learning, focusing on setting up an experimental framework for model assessment and selection. It discusses the evaluation of models to determine their performance and the generalization error. Topics include the use of training, validation, and test sets, cross-validation, stratification, and the bootstrap method for estimating errors. The lecture emphasizes the distinction between model selection and evaluation, highlighting the risks of over-learning and the need for unbiased performance estimation.