What is euclidean distance in reference to machine learning?

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krishnika · Answer 1 · 2020-01-16T07:03:36+0000

Euclidean distance is one of the major parameters while working with k nearest neighbor. It serves as the default distance between two sample spaces. Another prominent example is hierarchical clustering, agglomerative clustering (complete and single linkage) where you want to find the distance between clusters.

It represents the distance between two sample points in an n-dimensional space.

The formula for Euclidean distance is calculated as :

${\begin{aligned}d(\mathbf {p} ,\mathbf {q} )=d(\mathbf {q} ,\mathbf {p} )&={\sqrt {(q_{1}-p_{1})^{2}+(q_{2}-p_{2})^{2}+\cdots +(q_{n}-p_{n})^{2}}}\\[8pt]&={\sqrt {\sum _{i=1}^{n}(q_{i}-p_{i})^{2}}}.\end{aligned}}$

What is euclidean distance in reference to machine learning?

What is euclidean distance in reference to machine learning?

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