Nearest Neighbor Classifier: Curse of Dimensionality

This lecture delves into the nearest neighbor classifier method, explaining its application in regression and classification. The instructor discusses the impact of dimensionality on the method's performance, emphasizing the importance of spatial correlation between labels. The curse of dimensionality is explored, highlighting how the method's effectiveness diminishes as the dimensionality increases. The lecture provides insights into the bias-variance trade-off and the generalization error bound for the nearest neighbor method, showcasing the need for a large number of observations relative to the dimension for optimal performance.