Building a UMAP using Seaborn

In this recipe, we’ll learn about a newer and very visually appealing clustering algorithm called UMAP, using our breast cancer dataset!

’ UMAP is the Uniform Manifold Approximation & Projection. It is useful to understand the structure of higher-dimensional data, even when it is non-linear. UMAP essentially takes a high dimensional space and represents it using the most equivalent lower-dimensional graph it can find. It is also fast and efficient.

Getting Started

The code for this recipe can be found in Ch04/Ch04-6-seaborn.ipynb.

You will need to install the seaborn and umap-learn packages if you don’t already have them. You can do this from the terminal by typing:

pip install seaborn
pip install umap-learn

Or you can install these from the notebook like this:

! pip install umap-learn
! pip install seaborn! pip install ipywidgets

This installs the umap package. We also install seaborn here, although you may already have it installed...