juniorK-Nearest Neighbors
What is K-Nearest Neighbors (KNN) and how does it work?
Updated May 16, 2026
Short answer
KNN is a supervised algorithm that predicts a label based on the majority class (or average value) of its nearest neighbors.
Deep explanation
KNN is a lazy learning algorithm that does not build an explicit model during training. Instead, it stores the dataset and computes distances at inference time. Prediction is made by finding the k closest data points using a distance metric like Euclidean distance and aggregating their labels.
Real-world example
Movie recommendation systems suggesting similar movies based on user preferences.
Common mistakes
- Assuming KNN trains a model like linear regression.
Follow-up questions
- Why is KNN called a lazy algorithm?
- What is the role of K?