What is K-Nearest Neighbors (KNN) Algorithm in Machine Learning? An Essential Guide

The K-Nearest Neighbors (KNN) algorithm is a supervised machine learning technique used for classification and regression problems. It is categorized as a lazy learner, meaning it only stores the training dataset without going through a training stage. KNN works by estimating the likelihood that an unobserved data point will belong to one of two groups based on its nearest neighbors in the dataset. The algorithm uses a voting mechanism where the class with the most votes is assigned to the relevant data point. Different distance metrics can be used to determine whether or not a data point is a neighbor, such as Euclidean, Manhattan, Hamming, Cosine, Jaccard, and Minkowski distances. KNN can be improved by normalizing data on the same scale, tuning hyperparameters like K and distance metric, and using techniques like cross-validation to test different values of K. The algorithm is time-efficient, simple to tune, and easily adaptable to multi-class problems but may not perform well with high-dimensional or unbalanced data.