Shameless self plug here, but I saw this today and figured I should link to an interactive overview article "Understanding UMAP" my colleagues and I put togethe

Shameless self plug here, but I saw this today and figured I should link to an interactive overview article "Understanding UMAP" my colleagues and I put together: https://pair-code.github.io/understanding-umap/ https://pair-code.github.io/understanding-umap/ UMAP is a really useful piece in the modern data science toolkit, and despite its power it's surprisingly simple and elegant. But as with all dimensionality reduction techniques, there's a lot of ways to misread the results. High dimensional data behaves very counterintuitively, and any reduction in dimensionality fundamentally distorts the original data in some way. Understanding the fundamental concepts behind UMAP and exploring how it works is the best way to develop an intuition of what the technique can and can't tell you about your data.