Model Evaluation and Selection

This lecture covers the importance of model evaluation and selection in machine learning, emphasizing the need to estimate the generalization error accurately. Topics include the use of training, validation, and test datasets, the concept of cross-validation, and methods for variable selection. The instructor discusses the pitfalls of overfitting and the significance of separating the training and test datasets to prevent biased model evaluation.