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umap-learn

Reduce high-dimensional datasets to lower-dimensional embeddings with UMAP.

Updated Mar 15, 2026
Search Tags:#machine learning#visualization#dimensionality reduction#clustering#umap#manifold learning
One-click install
npx skills add https://github.com/sagunkayastha/claude_skills_collection --skill umap-learn-sagunkayastha
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/sagunkayastha/claude_skills_collection/tree/main/machine-learning-ai/umap-learn
Command: npx skills add https://github.com/sagunkayastha/claude_skills_collection --skill umap-learn-sagunkayastha

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies high-dimensional data by reducing it to a lower-dimensional space, making it easier to visualize, cluster, and analyze complex datasets.

Core Features & Use Cases

  • Dimensionality Reduction: Apply UMAP for fast, nonlinear manifold learning.
  • Visualization: Create 2D or 3D embeddings to visualize complex data structures.
  • Clustering Preprocessing: Prepare data for density-based clustering algorithms like HDBSCAN.
  • Feature Engineering: Generate lower-dimensional features for downstream machine learning models.
  • Use Case: Visualize a dataset of customer demographics in 2D to identify distinct customer segments.

Quick Start

Use the umap-learn skill to reduce the dimensionality of the provided data to 2 components for visualization.

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?▼

Dimensionality reduction for visualization is achieved by applying UMAP to transform high-dimensional datasets into 2D or 3D embeddings. This preserves both local and global data structures, making complex manifolds easier to visualize and analyze.

Can I use UMAP for clustering preprocessing with HDBSCAN?▼

UMAP works as clustering preprocessing by generating lower-dimensional embeddings that prepare high-dimensional data for density-based clustering. Applying UMAP before HDBSCAN helps identify distinct segments by simplifying the manifold structure.

What is the best way to preserve local and global structure in manifold learning?▼

Manifold learning with UMAP preserves local and global structure by using nonlinear dimensionality reduction to map high-dimensional data into a lower-dimensional space. This approach maintains the underlying topology better than linear techniques.

Does UMAP support feature engineering for downstream machine learning models?▼

UMAP supports feature engineering by generating lower-dimensional features from high-dimensional datasets. These reduced embeddings can be fed into downstream machine learning models to improve processing efficiency and performance.

Why use UMAP over scikit-learn for nonlinear dimensionality reduction?▼

UMAP provides fast, scalable nonlinear dimensionality reduction compared to scikit-learn alternatives. It effectively preserves global structure while maintaining local relationships, making it suitable for large datasets requiring manifold learning.

What dependencies do I need to run UMAP for dimensionality reduction?▼

Dimensionality reduction with UMAP requires the umap-learn library, along with numba for performance, scikit-learn for machine learning integration, matplotlib for plotting, and optionally hdbscan and tensorflow for extended workflows.

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