Umap Learn

UMAP is a dimensionality reduction algorithm that's faster than t-SNE and actually scales beyond 2D visualization. You'd use this when you need to compress high-dimensional data for plotting, preprocessing before HDBSCAN clustering, or feature engineering in ML pipelines. The key insight here is that visualization settings (n_neighbors=15, min_dist=0.1, 2D) are different from clustering settings (n_neighbors=30, min_dist=0.0, 5-10D). It supports supervised and semi-supervised modes by passing labels during fitting, which is surprisingly useful for metric learning. Always standardize your input data first. The documentation does a solid job explaining the four main parameters and why you'd tune them differently depending on whether you care about local detail or global structure.

Install to Claude Code

npx -y skills add k-dense-ai/scientific-agent-skills --skill umap-learn --agent claude-code