Study
Machine Learning · k-nearest neighbours
k-NN classifies by majority vote of the closest training points; why scaling matters and how k controls the bias–variance trade-off.
- k-NN has no training step: it stores the data and votes among the k closest points at prediction time.
- Without feature scaling, the feature with the largest units dominates the distance.