Learn how K-NN(K nearest neighbor) work. K-Nearest Neighbor is a Lazy learning algorithm, used in machine learning and other data mining field. It's sensitive to outliers . Algorithm is sensitive to outliers, since a single mislabeled example dramatically changes the class boundaries. Anomalies affect the method significantly, because k-NN gets all the information from the input, rather than from an algorithm that tries to generalize data Advantages and Disadvantages of KNN Algorithm in Machine Learning No Training Period: KNN is called Lazy Learner (Instance based learning). ... Since the KNN algorithm requires no training before making predictions, new data can be added seamlessly which will not impact the accuracy of the algorithm.