Simple Explanation of the K-Nearest Neighbors (KNN) Algorithm

The video tutorial explains the K Nearest Neighbours (KNN) algorithm, a simple method for classifying data based on proximity to known data points. It uses an imaginary dataset with features like body size and ear size to classify animals as bunnies, cats, or dogs. The tutorial demonstrates how different K values affect classification outcomes, highlighting that a small K value considers fewer neighbors, while a large K value may skew results towards the majority class. It also discusses the challenge of selecting an optimal K value for accurate classification.

Explain how the K Nearest Neighbours algorithm classifies an animal as a Bunny, cat, or dog.

What is a common challenge when implementing the KNN algorithm?