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K Nearest Neighbors

K Nearest Neighbors

Hello! In this post, we will be exploring a very simple classification algorithm called 'K Nearest Neighbors'.

How K Nearest Neighbors work:







The green point above is the testing data and the blue and red points are training data in different classes.

The way KNN works is by taking the Euclidean distance from your test data(green point) to each of your training data and classify your test data by the class of the nearest point to the testing data.

Euclidean Distance:

The Euclidean distance is basically the distance between two points on the Euclidean space. To put simply, the distance between two points. 

Formula:





K

K: Parameter that takes a certain number of nearest points to use for the classification process. 

K basically takes 'k' number of nearest points(training data) and classifies the test data by the class that has the highest vote. For example, if you set K=3 and you take 3 closest training data from your test data and get 2 points in class blue and 1 in class red, then you would classify the test data blue.  

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