Model Selection: Generalization and Validation

This lecture covers the concepts of generalization, model selection, and validation in machine learning. The instructor explains the importance of assessing the quality of a model, the process of model selection, and the use of cross-validation to ensure unbiased estimates of the generalization error. By splitting the data into training and test sets, and using techniques like K-fold cross-validation, the lecture demonstrates how to choose the best model parameter and evaluate its performance accurately.