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umap-learn

Perform non-linear dimensionality reduction with UMAP to project high-dimensional data into lower-dimensional spaces.

2|Updated Jun 4, 2026
Search Tags:#data analysis#visualization#dimensionality reduction#clustering#umap#manifold learning
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill umap-learn-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/umap-learn
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill umap-learn-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires umap-learn, scikit-learn, matplotlib, numpy, and includes references (resource) components.

What problem does it solve?

This skill addresses the challenge of visualizing and analyzing high-dimensional data by providing a fast, scalable method for non-linear dimensionality reduction that preserves both local and global structure.

Core Features & Use Cases

  • Dimensionality reduction for 2D/3D visualization of complex datasets.
  • Preprocessing for density-based clustering algorithms like HDBSCAN.
  • Supervised and semi-supervised embedding to guide manifold learning with label information.
  • Efficient transformation of new, unseen data into existing embedding spaces.

Quick Start

Use the umap-learn skill to perform dimensionality reduction on the provided dataset and visualize the results in two dimensions.

Frequently Asked Questions about umap-learn

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

FAQPage Schema
How do I perform non-linear dimensionality reduction for high-dimensional data visualization?▼

Non-linear dimensionality reduction projects high-dimensional data into lower-dimensional spaces using the UMAP algorithm, preserving both local and global structure for 2D or 3D visualization.

What is the best way to prepare data for density-based clustering algorithms?▼

Preprocessing high-dimensional data with UMAP generates clustering-optimized embeddings, serving as effective input for density-based clustering algorithms like HDBSCAN.

Can I use supervised learning to guide manifold learning and embedding?▼

Yes, supervised and semi-supervised embedding workflows guide manifold learning by incorporating label information to shape the projection of high-dimensional data into lower dimensions.

Does UMAP support transforming new unseen data into an existing embedding space?▼

Yes, UMAP supports efficient transformation of new, unseen data points into previously established embedding spaces without needing to recompute the entire manifold approximation.

Do I need scikit-learn and numpy to execute manifold approximation tasks?▼

Yes, executing manifold approximation and projection tasks requires scikit-learn, numpy, and umap-learn dependencies to process the high-dimensional datasets.

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