Model Evaluation: K-Nearest Neighbor

This lecture covers the evaluation of models, focusing on the K-Nearest Neighbor algorithm. It explains how to choose the optimal k value using cross-validation, different similarity metrics, and model assessment techniques like leave-one-out cross-validation. The lecture also delves into the theory of expected loss, training error, and information criteria such as AIC and BIC. It concludes with a detailed discussion on performance metrics for classification models, including accuracy, precision, recall, ROC curves, and the importance of comparing model performance to baseline models.