UMAP is a dimensionality reduction library. It seems like a lot of words, but it basically takes a complicated dataset with many variables and reduces it down to something much simpler without losing the fundamental characteristics.
We next setup the defaults for plotting and get some data to work with. We'll look at the Iris dataset. It isn't very representative in terms of real world data since btoht the number of points and features are small, but it will illustrate what is going on with dimensionality reduction.
;;; 150 samples with 2 column. Each row of the array is a 2-dimensional representation of the corresponding flower. Thus we can plot the embedding as a standard scatterplot and color by the target array (since it applies to the transformed data which is in the same order as the original).