The best choice of k depends upon the data; generally, larger values of k reduce the effect of noise on the classification,[REF] but make boundaries between classes less distinct.
The accuracy of the k-NN algorithm can be severely degraded by the presence of noisy or irrelevant features, or if the feature scales are not consistent with their importance.