👉 Neighbors is a powerful, open-source algorithm for finding the most similar neighbors in a dataset. It operates by constructing a k-nearest neighbors (k-NN) tree, where each node represents a data point and stores the k nearest points in its leaf nodes. The algorithm begins by building this tree using a distance metric, typically Euclidean, to determine the proximity between data points. Once the tree is constructed, querying for the k-nearest neighbors involves traversing the tree from the root to the leaf nodes, collecting all points at each internal node until reaching the leaf nodes, which contain the actual neighbors. Neighbors excels in scenarios where real-time predictions are needed, as it avoids the computational overhead of training a model and instead relies on precomputed distances stored in the tree structure. This makes it particularly suitable for applications like image recognition, recommendation systems, and anomaly detection where quick retrieval of similar instances is crucial.
