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K-Nearest Neighbors Classifier

K-Nearest Neighbors Classifier is a medium coding interview question covering machine learning, vector search, arrays — the kind asked at FAANG (Facebook/Meta, Apple, Amazon, Netflix, Google) and AI-first companies like Anthropic, OpenAI, and Nvidia.

machine learningvector searcharrays

K-Nearest Neighbors Classifier — Problem Statement

Implement a K-Nearest Neighbors (KNN) classifier from scratch.

Given a training set of (feature_vector, label) pairs and a query point, classify the query by majority vote among its k nearest neighbors using Euclidean distance.

Example: train=[([0,0],0),([5,5],1),([1,1],0)], query=[2,2], k=2 -> label 0

This is a fundamental non-parametric ML algorithm. No training phase -- all work is at inference time.

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