i noticed that in KNN, it is not effected by outliers as much , it is considered as its advantage and at the same time some people marked this point as its disadvantage that it is sensitive to outliers , i want to get my confusion clear with suitable example?
KNN is outlier sensitive. See the book in the attachment. The programs are available in R package cluster. Best wishes, David Booth
One of the many issues that affect the performance of the kNN algorithm is the choice of the hyperparameter k. If k is too small, the algorithm would be more sensitive to outliers. If k is too large, then the neighborhood may include too many points from other classes.
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
- Badges
- Science topic
- Similar topics
- Engineering
- Control Theory
- Control Systems
- Control Systems Engineering