K-Nearest Neighbors (KNN)

KNN is one of the simplest and most intuitive machine learning algorithms. The idea: you are like your neighbors.

The Core Idea

To classify a new point, look at the K closest points in the training data and vote.

How KNN Works Step by Step

Choosing K

The value of K affects the model significantly.

Distance Metrics

Feature Scaling is Critical!

KNN Pros and Cons

Key Takeaways

• KNN classifies by finding the K most similar training examples
• No training - it just stores the data (lazy learning)
• K controls the trade-off between noise sensitivity and oversimplification
• Feature scaling is essential - different scales distort distances
• Use Euclidean distance for continuous features
• Choose odd K for binary classification to avoid ties
• Works for both classification and regression

You've completed Module 5! Next, we'll explore Decision Trees and Random Forests!