Model Evaluation

This lecture covers the concepts of underfitting and overfitting in machine learning models, discussing the impact of hyperparameters on model flexibility and the bias-variance trade-off. It explains the process of fitting multiple training sets, the bias-variance decomposition, and the application of these concepts to handwritten digit recognition. The lecture also delves into the validation set approach, cross-validation, and model tuning techniques, providing insights into the challenges and considerations in supervised learning.