Umap, Machine Learning, AI, Dimensionality Reduction.
load_iris()["target"] # create transformer umap = UMAP(n_components=2, n_neighbors=8, random_state=42) # transform data, # note that you can´t get more features then what you started out with. reduced_data = umap.fit_transform(data) # Lets create an image of the information dense PCA features