K Nearest Neighbor Algorithm is one of the simplest classification algorithm. In other words, KNN is a classification/supervised learning algorithm.
K Nearest Neighbor is a non-parametric, lazy learning algorithm.
Lazy learning refers to the fact that the algorithm does not build a model until a prediction is required. It is lazy because it only does work at the last second. This has the benefit of only including data relevant to the unseen data, called a localized model.
The principle behind nearest neighbor methods is to find a predefined number of training samples closest in distance to the new point, and predict the label from these.
Supervised neighbors-based learning is used in: </s