Before effectively applying the k nearest neighbor classification technique, the variables need to be
A) standardized
B) normalized
C) randomized
D) trimmed

Question

Quizplus · Accepted Answer

Standardizing the variables is the best choice as it will transform the variables to have a mean of 0 and standard deviation of 1, which is necessary for k nearest neighbor to work effectively as it is sensitive to differences in scales of variables. Normalizing could also be a good choice depending on the context, but standardizing is a more widely used approach. Randomizing and trimming are not relevant for preparing variables for k nearest neighbor classification.

Before Effectively Applying the K Nearest Neighbor Classification Technique, the Variables