What is k-Nearest Neighbors?

K-Nearest Neighbors (k-NN) is a simple, supervised machine learning algorithm used for both classification and regression. It works by finding the 'k' nearest data points (neighbors) in the training dataset to a new, unlabeled data point, based on a distance metric (e.g., Euclidean distance). For classification, the new point is assigned the class most frequent among its 'k' neighbors. For regression, the predicted value is the average (or weighted average) of the values of its 'k' neighbors. k-NN is often used for its simplicity and ease of implementation, and can be applied in various fields such as pattern recognition, image recognition, and recommendation systems.