Python K-NN

 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.