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Introduction to K-Nearest Neighbors

Introduction to K-Nearest Neighbors

This lesson will introduce the k-nearest neighbors technique and the steps involved in its implementation.

We'll cover the following...

Quick overview

Our next supervised learning classification technique is K-Nearest Neighbors (k-NN), which classifies new unknown data points based on their proximity to known data points. This classification process is determined by setting the k number of data points closest to the target data point. For example, if you set k to 3, k-NN analyzes the nearest three data points (neighbors) to the target data point.

Example

Given the following dataset and predict the class for p (p1=3p_1=3 and p2=7p_2=7) and value of k=2k=2.

Sr. P1 P2 Class
i 7 7 B
ii 7 4 B
iii 3 4 A
iv 1 4 A
  • Euclidean distance

d(p,q)=i=1n(qipi)2d\left( p,q\right) = \sqrt {\sum _{i=1}^{n} \left( q_{i}-p_{i}\right)^2 } ...