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HaykTarkhanyan
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umap-learn

Reduce high-dimensional data to low-dimensional embeddings with UMAP.

Updated Mar 31, 2026
Search Tags:#visualization#dimensionality-reduction#umap#manifold-learning#parametric-umap#clustering-preprocessing
One-click install
npx skills add https://github.com/HaykTarkhanyan/dst_research --skill umap-learn-hayktarkhanyan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/HaykTarkhanyan/dst_research/tree/main/.claude/skills/umap-learn
Command: npx skills add https://github.com/HaykTarkhanyan/dst_research --skill umap-learn-hayktarkhanyan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Dimensionality reduction of high-dimensional data into low-dimensional embeddings for visualization and downstream analysis.

Core Features & Use Cases

  • Fast dimensionality reduction for 2D/3D visualizations
  • Supports supervised, semi-supervised, and parametric UMAP variants
  • Sklearn-compatible pipelines and clustering preprocessing (e.g., HDBSCAN)

Quick Start

Fit UMAP to your dataset and obtain a 2D embedding.

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 visualization?▼

UMAP reduces high-dimensional data to 2D or 3D embeddings for visualization while preserving local and global structure. It provides fast, scalable embeddings that maintain meaningful relationships in your data.

Can I use UMAP for clustering preprocessing?▼

Yes, UMAP is commonly used for clustering preprocessing. By reducing high-dimensional data to lower-dimensional embeddings, it prepares datasets for clustering algorithms like HDBSCAN to identify distinct groupings more effectively.

Does this dimensionality reduction approach work with sklearn pipelines?▼

Yes, UMAP is fully sklearn-compatible. You can integrate it into standard sklearn pipelines for both supervised and semi-supervised learning workflows, including feature engineering and general data transformation tasks.

What parameters do I need to tune for manifold learning?▼

Key parameters for manifold learning include n_neighbors, min_dist, n_components, and the distance metric. Tuning these controls the balance between local and global structure preservation in the resulting embeddings.

How does DensMAP improve density preservation in embeddings?▼

DensMAP is an optional UMAP mode that preserves local density information alongside structural relationships. It ensures that variations in data density are maintained in the low-dimensional embeddings, providing more accurate visualizations.

When should I use parametric UMAP instead of standard UMAP?▼

Use parametric UMAP when you need a learned transformation function to embed new, unseen data points. Standard UMAP relies on direct graph optimization, while the parametric variant uses neural networks for added flexibility.

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