Phase 1: Classification Algorithms · ~90 minutes · Python

K-Nearest Neighbors and Distances

Store everything. Predict by looking at your neighbors. The simplest algorithm that actually works.

Hiring signal: Understanding of k-nearest neighbors and distances internals

What you will learn

Implement KNN classification and regression from scratch with configurable K and distance-weighted voting
Compare L1, L2, cosine, and Minkowski distance metrics and select the appropriate one for a given data type
Explain the curse of dimensionality and demonstrate why KNN degrades in high-dimensional spaces
Build a KD-tree for efficient nearest neighbor search and analyze when it outperforms brute-force

Introduction

Type: Build Language: Python Prerequisites: Phase 1 (Lesson 14 Norms and Distances) Time: ~90 minutes

Learning Objectives

Implement KNN classification and regression from scratch with configurable K and distance-weighted voting
Compare L1, L2, cosine, and Minkowski distance metrics and select the appropriate one for a given data type
Explain the curse of dimensionality and demonstrate why KNN degrades in high-dimensional spaces
Build a KD-tree for efficient nearest neighbor search and analyze when it outperforms brute-force

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.