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umap-learn

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

Updated Aug 23, 2026
Search Tags:#visualization#preprocessing#clustering#machine-learning#dimensionality-reduction#umap#manifold-learning
One-click install
npx skills add https://github.com/Org-GAgent/result-interpreter --skill umap-learn-org-gagent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/Org-GAgent/result-interpreter/tree/main/.skills/scientific-skills/umap-learn
Command: npx skills add https://github.com/Org-GAgent/result-interpreter --skill umap-learn-org-gagent

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

UMAP reduces high-dimensional data to a low-dimensional embedding for visualization, clustering, and downstream machine-learning tasks, enabling faster insights from complex datasets.

Core Features & Use Cases

  • Dimensionality reduction for 2D/3D visualization and exploratory data analysis.
  • Preprocessing for clustering and downstream ML models, including handling nonlinear manifolds.
  • Support for supervised and semi-supervised embeddings, parametric UMAP, and alignment across related datasets.

Quick Start

Fit a UMAP model to your data and transform it to 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 using UMAP?▼

UMAP reduces high-dimensional data to a low-dimensional embedding for 2D or 3D visualization. You fit the model to your dataset and apply transform to obtain the embedding for exploratory analysis.

What is the best way to preprocess data for clustering on nonlinear manifolds?▼

Dimensionality reduction via UMAP preprocesses data for clustering by mapping nonlinear manifolds to a lower-dimensional space. This simplifies complex datasets for downstream machine-learning models.

Can I apply supervised or semi-supervised embeddings to my dataset?▼

Yes, UMAP supports supervised and semi-supervised embeddings for your dataset. It also supports parametric UMAP and alignment across related datasets to preserve structural relationships.

What UMAP parameters control the structure of the low-dimensional embedding?▼

Core UMAP parameters like n_neighbors, min_dist, n_components, and metric control the low-dimensional embedding structure. Adjusting these tunes how local and global data structures are preserved.

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

UMAP supports transform to project new data points into an existing low-dimensional embedding. It also provides inverse_transform to map points back to the original high-dimensional space.

When should I use UMAP over other dimensionality reduction techniques for machine learning?▼

Use UMAP for machine learning when you need fast dimensionality reduction that preserves nonlinear manifold structure. It is particularly effective for visualization and clustering preprocessing across complex datasets.

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