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umap-learn

Compute low-dimensional embeddings from high-dimensional data using UMAP.

4|1|Updated Jun 18, 2025
Search Tags:#visualization#clustering#machine-learning#dimensionality-reduction#umap#manifold-learning
One-click install
npx skills add https://github.com/HolobiomicsLab/Toolomics --skill umap-learn-holobiomicslab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/HolobiomicsLab/Toolomics/tree/main/mcp_host/skills/scientific-skills/scientific-skills/umap-learn
Command: npx skills add https://github.com/HolobiomicsLab/Toolomics --skill umap-learn-holobiomicslab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

UMAP reduces high-dimensional data into low-dimensional embeddings to enable fast visualization, exploration, and downstream analysis.

Core Features & Use Cases

  • Dimensionality reduction for 2D/3D visualization, clustering prep (e.g., HDBSCAN), and downstream ML workflows.
  • Supervised and Parametric UMAP variants to incorporate labels or learn mappings for new data.
  • Efficient, sklearn-compatible workflows that integrate into pipelines.

Quick Start

Install UMAP-learn and run a simple embedding on your dataset.

Frequently Asked Questions about umap-learn

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce high-dimensional data for 2D visualization using manifold learning?▼

Dimensionality reduction for 2D visualization computes low-dimensional embeddings from high-dimensional data using UMAP, enabling fast exploration and downstream analysis workflows.

Can I use dimensionality reduction embeddings as preprocessing for clustering?▼

Yes, dimensionality reduction embeddings serve as preprocessing for clustering, preparing data for HDBSCAN and other downstream machine-learning workflows.

Does UMAP work with sklearn-compatible pipelines?▼

UMAP works with sklearn-compatible pipelines, allowing efficient integration into existing machine-learning workflows with configurable parameters like n_neighbors and min_dist.

What is the best way to map new data into an existing low-dimensional embedding?▼

Parametric UMAP learns mappings for new data, allowing you to map unseen high-dimensional data points into an existing low-dimensional manifold space.

Can I incorporate labels into dimensionality reduction for supervised learning?▼

Supervised UMAP variants incorporate labels into the dimensionality reduction process, allowing the manifold learning to use class information for guided embeddings.

What parameters do I configure to control manifold learning embeddings?▼

Configurable parameters like n_neighbors, min_dist, and n_components control the manifold learning embeddings, allowing you to adjust the balance between local and global structure.

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