umap-learn

Reduce high-dimensional data to 2D or 3D embeddings with UMAP.

557|98|Updated Nov 7, 2025

Search Tags:#machine learning #visualization #dimensionality reduction #clustering #umap #manifold learning

Ready to run

npx skills add https://github.com/jimmc414/Kosmos --skill umap-learn-jimmc414

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/umap-learn
Command: npx skills add https://github.com/jimmc414/Kosmos --skill umap-learn-jimmc414

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of understanding and visualizing high-dimensional data by reducing its complexity to a lower-dimensional space while preserving essential structure.

Core Features & Use Cases

Dimensionality Reduction: Apply UMAP for fast, nonlinear manifold learning.
Visualization: Create 2D or 3D embeddings for data exploration.
Clustering Preprocessing: Prepare data for density-based clustering algorithms like HDBSCAN.
Supervised Learning: Guide embeddings with label information for class separation.
Use Case: Visualize a complex dataset of customer behaviors in 2D to identify distinct customer segments, or use its embeddings as features for a machine learning model.

Quick Start

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

Frequently Asked Questions about umap-learn

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

FAQPage Schema

What is the best way to preserve data structure during nonlinear dimensionality reduction?▼

To preserve data structure during nonlinear dimensionality reduction, UMAP allows parameter tuning and supports various distance metrics. Adjusting these parameters ensures the low-dimensional projection maintains the topological relationships present in the original high-dimensional space.

How do I perform dimensionality reduction for visualizing high-dimensional data?▼

Dimensionality reduction for visualization is achieved by applying UMAP to transform high-dimensional data into 2D or 3D embeddings while preserving its essential nonlinear structure. This allows you to explore complex datasets and identify distinct segments visually.

Can I use UMAP embeddings as preprocessing for clustering algorithms?▼

Yes, UMAP embeddings can be used for clustering preprocessing to prepare high-dimensional data for density-based clustering algorithms. By reducing dimensions first, clustering algorithms can more efficiently identify distinct groupings within the transformed space.

Does UMAP support supervised learning for class separation?▼

UMAP supports supervised and semi-supervised learning by using label information to guide the embedding process. This approach forces the manifold learning to optimize for class separation, resulting in distinct visual clusters for each labeled category.

How can I visualize customer behavior segments using manifold learning?▼

Manifold learning visualizes customer behavior segments by applying nonlinear dimensionality reduction to complex behavioral data. UMAP projects this data into a 2D space, allowing you to visually identify distinct customer groupings based on their structural similarities.

What is the best way to preserve data structure during nonlinear dimensionality reduction?▼