k-NN

2000년 도입 · 논문 192편에서 사용

$k$-Nearest Neighbors is a clustering-based algorithm for classification and regression. It is a a type of instance-based learning as it does not attempt to construct a general internal model, but simply stores instances of the training data. Prediction is computed from a simple majority vote of the nearest neighbors of each point: a query point is assigned the data class which has the most representatives within the nearest neighbors of the point. Source of Description and Image: scikit-learn