K-Nearest Neighbors (KNN) is a simple yet effective machine learning algorithm used for both classification and regression tasks. KNN operates on the principle that objects or data points in a dataset are more similar to those in their proximity. In the context of classification, KNN assigns a class label to a data point based on the majority class among its k-nearest neighbors, where k is a user-defined parameter. For regression, KNN calculates the average or weighted average of the target values of its k-nearest neighbors to predict the value of the data point. The “nearest neighbors” are determined by measuring the distance between data points in a feature space, often using Euclidean distance, though other distance metrics can be employed as well.
KNN is a non-parametric and instance-based algorithm, meaning it doesn’t make underlying assumptions about the data distribution. It can be applied to various types of data, including numerical, categorical, or mixed data, and is easy to implement. However, KNN’s performance is highly dependent on the choice of k and the distance metric, and it can be sensitive to the scale and dimensionality of the features. It’s suitable for small to medium-sized datasets and may not perform optimally on high-dimensional data. Despite its simplicity, KNN is a valuable tool in machine learning and is often used for tasks such as recommendation systems, image classification, and anomaly detection.