UMAP is typically used for embeddings that get displayed to a human, so 2 or 3 dimensions is typical.
It doesn’t really suit inference/prediction because you can’t really add new data without influencing the embedding values of the other data. It’s not like PCA where you can learn a projection once and then map new data points to the same embedding space.
It doesn’t really suit inference/prediction because you can’t really add new data without influencing the embedding values of the other data. It’s not like PCA where you can learn a projection once and then map new data points to the same embedding space.