Agent skill · data analytics · davila7
umap-learn
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Why this skill is useful
Provides specific parameter tuning guidelines and usage patterns for UMAP that enhance the AI's ability to apply dimensionality reduction effectively.
What it needs
Requires umap-learn installed locally. About 7k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
UMAP-Learn Overview UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique for visualization and general non-linear dimensionality reduction. Apply this skill for fast, scalable embeddings that preserve local and global structure, supervised learning, and clustering preprocessing. Quick Start Installation Basic Usage UMAP follows scikit-learn conventions and can be used as a drop-in replacement for t-SNE or PCA. Critical preprocessing requirement: Always standardize features to comparable scales before applying UMAP to ensure equal weighting across dimensions. Typical Workflow Parameter Tuning Guide UMAP has four primary parameters that control the embedding behavior. Understanding these is crucial for effective usage. nneighbors (default: 15) Purpose: Balances local versus global structure in the embedding. How it works: Controls the size of the local neighborhood UMAP examines when learning manifold structure. Effects by value: Low values (2-5): Emphasizes fine local detail but may fragment data into disconnected components Medium values (15-20): Balanced view of both local structure and global relationships (recommended starting point) High values (50-200): Prioritizes broad topological structure at the expense of fine-grained details Recommendation: Start with 15 and adjust based on results. Increase for more global structure, decrease for more local detail. mindist (default: 0.1) Purpose: Controls how tightly points cluster in the low-dimensional space. How it works: Sets the minimum distance apart that points are allowed to be in the output representation. Effects by value: Low values (0.0-0.1): Creates clumped embeddings useful for clustering; reveals fine topological details High values (0.5-0.99): Prevents tight packing; emphasizes broad topological preservation over local structure Recommendation: Use 0.0 for clustering applications, 0.1-0.3 for visualization, 0.5+ for loose structure. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/umap-learn