K-Nearest Neighbor Algorithm: Classification and Regression Implementation

In machine learning, the K-Nearest Neighbor (K-NN) algorithm serves as a fundamental classification and regression method. The algorithm operates by calculating distances between target samples and training samples, selecting the K closest training instances based on distance metrics, and making predictions using their labels through majority voting (classification) or averaging (regression). K-NN proves particularly effective for datasets with distinct categorical boundaries but may suffer from the curse of dimensionality when handling high-dimensional data. Consequently, researchers have developed numerous enhanced variants, including kernel-based K-NN implementations that transform distance calculations using kernel functions, and locally weighted K-NN approaches that assign higher weights to closer neighbors through Gaussian or inverse-distance weighting schemes. Core implementation typically involves distance computation using methods like Euclidean (sqrt(sum((x-y)^2))) or Manhattan distance, efficient nearest neighbor search via KD-Tree or Ball Tree data structures for large datasets, and parameter optimization for K-value selection through cross-validation techniques.