UMAP, or Uniform Manifold Approximation and Projection, is a non-linear dimensionality reduction technique that helps visualize high-dimensional data in lower dimensions, typically two or three. By preserving the local structure of the data while maintaining its global features, UMAP is particularly useful in supervised learning tasks where understanding complex relationships in data is crucial for model training and evaluation.
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