Instance-based learning (aka Case-based or Memory-based or non-parametric)

This document provides an overview of instance-based learning techniques. It begins by introducing 1-nearest neighbor classification and regression, which makes predictions based on the single closest training example. It then discusses how k-nearest neighbor addresses some of the issues with 1-NN by considering the average output of the k closest examples. The document also covers kernel regression, which weights all training examples based on their distance from the query point. It demonstrates how varying the kernel width parameter and query point affects the predictions. Instance-based learning relies on storing past examples and making predictions by comparing new examples to similar stored examples.