About
UMAP-Learn provides a robust framework for non-linear dimensionality reduction, allowing developers to project complex, high-dimensional datasets into 2D/3D for visualization or lower-dimensional spaces for machine learning pipelines. This skill streamlines the implementation of UMAP for tasks like clustering preprocessing with HDBSCAN, supervised feature engineering, and parametric embedding using neural networks. It provides expert guidance on critical parameter tuning—such as n_neighbors and min_dist—to ensure the preservation of both local and global data structures during transformation.