K-Nearest Neighbors
K-Nearest Neighbors never trains. It stores the labeled data and answers any question by looking at the k most similar past examples: they vote (classification) or average (regression). You will see how to pick k, why distances demand scaling, which metric fits which data, and why "nearest" quietly breaks in high dimensions.
01.The Problem: Predict Without Building a Model
You're house-hunting. A new listing appears and you want a price guess.
What do you actually do? You don't fit any equation in your head.
You say: "It's like the three houses on Maple Street, but smaller."
That instinct is k-nearest neighbors.
What if the "model" is just... the data itself, plus a similarity search?
For most models (linear regression Topic 8, trees Topic 58, boosting Topic 60) training squeezes the data into a small set of parameters. Then the raw data is discarded. KNN refuses:
Why compress what you can just look up?
It stores every labeled point and, at prediction time, finds the k most similar stored points and copies their wisdom. No training pass, no coefficients, no assumptions about the shape of the classes. The bill arrives later — at query time — and Section 7 shows exactly how big it gets.
The catch you should already smell: everything hinges on what "similar" means. Choose the wrong notion of similar and the neighbors are nonsense. KNN spends the rest of this topic making that one idea careful.
KNN Inference and the k Slider 🕵️
KNN Inference and the k Slider 🕵️
Prediction is brute-force similarity: standardize, measure, take k, vote. The only real modeling decision is k and the distance function.
Unlock Topic #63: K-Nearest Neighbors
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