What is the primary consequence of failing to scale features before applying a K-Nearest Neighbors (KNN) algorithm?

Question

Data Science — Medium

What is the primary consequence of failing to scale features before applying a K-Nearest Neighbors (KNN) algorithm?

Accepted Answer

KNN relies on distance metrics like Euclidean distance. If one feature ranges from 0 to 1 and another from 0 to 1000, the latter will dominate the distance calculation, effectively ignoring the contribution of the smaller-scale feature. Scaling ensures all features contribute equally to the distance metric.