K-Nearest Neighbors (KNN)

Learn about K-Nearest Neighbors (KNN), a simple yet effective algorithm used for both classification and regression. Understand how KNN makes predictions based on the proximity of data points, and explore real-world use cases in recommendation systems and pattern recognition. Practice KNN implementations in Python using scikit-learn.

Introduction

Get introduced to K-Nearest Neighbors (KNN), a simple yet powerful algorithm for classification and regression.

Theory

Understand the theory behind KNN, focusing on distance metrics and decision boundaries.

scikit-learn Example

Tune and evaluate a leakage-safe K-nearest-neighbors classifier.

TensorFlow Guidance

Understand why classical KNN belongs in a neighbor-search library rather than a Keras model.

PyTorch Guidance

Understand why KNN is not a native PyTorch estimator and how to choose the right tool.

Common Mistakes & Best Practices

Prevent leakage and choose KNN scaling, distance, validation, and deployment settings correctly.

Comparison with Other Algorithms

Compare KNN with linear models, trees, SVMs, ensembles, and neural networks.