The lazy learners’ algorithm: Understanding K-Nearest Neighbour (KNN)
Simply put, KNN is a Supervised Machine Learning algorithm. But, do you know what that is? Let’s understand this through an example. Imagine that your computer is a child, you are his supervisor (guardian/parent/teacher), and you want the child (computer) to learn how a cat looks like. So, you show him different pictures of numerous animals including dogs, pigs, rabbits etc.
Both of you scan through these pictures, and you make him aware whether the picture is of a ‘cat’ or ‘not a cat’. After multiple repetitions of this exercise, when you show him a picture and ask ‘a cat?’ most probably they’ll give the correct response by either saying ‘yes, a cat’ or ‘no, not a cat’ depending upon the picture. This is supervised learning.
K-Nearest Neighbour or KNN algorithm is one of the simplest yet most powerful Machine Learning technique based on Supervised Learning Algorithms that use most similar historical examples to make predictions for new data. KNN algorithm simply stores all the available data and classifies a new data set based on its stored similarity metrics. It’s also smart enough to not make any assumptions, and hence is one of the most promising methods for accurate classification of data.
KNN can be applied for both classification and regression. This algorithm is widely used to solve classification problems. To know more about KNN, read Introduction to K-Nearest Neighbours (KNN) - Supervised Machine Learning Algorithm on IndiaAI, and build your basics through a simple overview of the K-Nearest Neighbour Algorithm.