k-Nearest Neighbors

Easy

The only major supervised family that never fits parameters. k-NN keeps the training set and defers every decision to prediction time, which makes it the honest baseline you run before anything else, and a fast lesson in why feature scaling, distance metrics, and the curse of dimensionality matter.

Learning Objectives

→Compute pairwise Euclidean distances with broadcasting instead of loops
→Classify by majority vote over the k nearest neighbours with a deterministic tie-break
→Explain why unscaled features silently destroy a distance-based model
→Read the bias-variance tradeoff off a sweep over k

Coding Problems (1)

k-Nearest Neighbors Classifier

~20 min· Easy

Concept Check

Optional multiple-choice questions on the ideas behind this section. Most useful after you have tried the coding problems above.