UMAP (Uniform Manifold Approximations and Projection)

UMAP (Uniform Manifold Approximation and Projection) is a technique for reducing complex data into simpler, visual forms. It analyzes the structure of high-dimensional data (many features) to find patterns and relationships, then maps these into lower dimensions (like 2D or 3D) for visualization. UMAP preserves the data's local and global structure, making clusters and patterns easier to see. It's widely used in fields like machine learning, biology, and data analysis to explore and understand large, complicated datasets more intuitively.