Understanding the Curse of Dimensionality and Model Selection in Regression
In the recent YouTube video, the discussion centers around nearest neighbor averaging and the complications arising from the curse of dimensionality in regression modeling. Here, I will consolidate the key concepts and quantitative examples discussed. Nearest Neighbor Averaging and Dimensionality Nearest neighbor averaging yields satisfactory results in low-dimensional spaces, typically when the number of predictors ( P ) is small (e.g., ( P \leq 4 )) and the number of observations ( n ) is sufficiently large.