UMAP, or Uniform Manifold Approximation and Projection, is a dimensionality reduction technique that helps visualize high-dimensional data in a lower-dimensional space, often two or three dimensions. It preserves the local structure of the data while capturing the global structure more effectively than other methods, making it particularly useful in machine learning and data science applications for exploring complex datasets.
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