Question: is knn sensitive to outliers?
Answer: 5. 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.