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Exploring whether Enriched Category Theory could provide the foundation of analternative approach to Machine Learning. This paper is the first to constructand motivate a Machine Learning algorithm solely with Enriched Category Theory.In order to supplement evidence that Category Theory can be used to motivaterobust and explainable algorithms, it is shown that a series of reasonableassumptions about a dataset lead to the construction of the Nearest NeighboursAlgorithm. In particular, as an extension of the original dataset usingprofunctors in the category of Lawvere metric spaces. This leads to adefinition of an Enriched Nearest Neighbours Algorithm, which consequently alsoproduces an enriched form of the Voronoi diagram. This paper is intended to beaccessible without any knowledge of Category Theory

Using Enriched Category Theory to Construct the Nearest Neighbour Classification Algorithm