K Nearest Neighbor Regressor, Explained: A Visual Guide with Code Examples
REGRESSION ALGORITHM
K Nearest Neighbor Classifier, Explained: A Visual Guide with Code Examples for Beginners
Building on our exploration of the Nearest Neighbor Classifier, let's turn to its sibling in the regression world. The Nearest Neighbor Regressor applies the same intuitive concept to predicting continuous values. But as our datasets get bigger, finding these neighbors efficiently becomes a real pain. That's where KD Trees and Ball Trees come in.
It's super frustrating that there's no clear guide out there that really explains what's going on with these algorithms. Sure, there are some 2D visualizations, but they often don't make it clear how the trees work in multidimensional setting.
Here, we will explain what's actually going on in these algorithms without using the oversimplified 2D representation. We'll be focusing on the construction of the trees itself and see which computation (and numbers) actually matters.
Definition
The Nearest Neighbor Regressor is a straightforward predictive model that estimates values by averaging the outcomes of nearby data points. This method builds on the idea that similar inputs likely yield similar outputs.
