Chapter 05: k-Nearest Neighbors (k-NN)

This chapter addresses \(k\)-nearest neighbors, a distance-based algorithm suited to both regression and classification. Predictions are made based upon neighboring observations, assuming feature similarity translates to target similarity.

Chapter 05.01: k-Nearest Neighbors (k-NN)
We demonstrate that distances in feature space are crucial in \(k\)-NN regression / classification and show how we can form predictions by averaging / majority vote. In this, \(k\)-NN is a very local model and works without distributional assumptions.