Individual skill

umap-learn

UMAP dimensionality reduction for visualization, clustering prep, and feature engineering. Fast nonlinear manifold learning preserving local and global structure. Standard UMAP (fit/transform, sklearn-compatible), supervised/semi-supervised, Parametric UMAP (NN encoder/decoder, TensorFlow), DensMAP (density), AlignedUMAP (temporal/batch). 15+ distance metrics, custom Numba metrics, precomputed distances. For linear reduction use PCA; for neighborhood graphs use sklearn NearestNeighbors.

skills/scientific-computing/umap-learn/SKILL.md

Source commit 55c1bd3e863d11bf5a853d24bcdda10c88e6eddf

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Nov 5, 2026

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