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umap-learn

Reduce high-dimensional data into lower-dimensional embeddings preserving neighborhood structure.

4|Updated Mar 2, 2026
Search Tags:#embeddings#dimensionality reduction#umap#hdbscan#manifold learning#clustering preprocessing#supervised umap
One-click install
npx skills add https://github.com/shushuzn/Rairos --skill umap-learn-shushuzn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/shushuzn/Rairos/tree/main/skills/umap-learn
Command: npx skills add https://github.com/shushuzn/Rairos --skill umap-learn-shushuzn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

UMAP-Learn reduces high-dimensional data into lower-dimensional embeddings that preserve meaningful structure, making complex patterns easier to visualize, cluster, and model.

Core Features & Use Cases

  • Fast non-linear dimensionality reduction for visualization and general embedding workflows
  • Structure-preserving embeddings that balance local and global relationships, controlled by parameters like n_neighbors and min_dist
  • Clustering preprocessing by generating higher-dimensional embeddings that often work better with density-based methods like HDBSCAN
  • Supervised and semi-supervised embedding by incorporating labels via y to improve class separation
  • Quick start to transform new data using fit then transform, enabling reuse of a learned manifold mapping
  • Optional advanced variants including Parametric UMAP and DensMAP for efficient inference and density preservation

Quick Start

Ask the AI to help you reduce your standardized dataset into 2D embeddings using UMAP with n_neighbors=15 and min_dist=0.1, then plot the result colored by your labels.

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 while preserving structure?▼

Dimensionality reduction for visualization is achieved by learning non-linear embeddings that preserve meaningful neighborhood structure. UMAP balances local and global relationships using configurable parameters like n_neighbors and min_dist to generate clear 2D or 3D plots.

Can I use UMAP embeddings as preprocessing for density-based clustering like HDBSCAN?▼

UMAP works well for clustering preprocessing by generating higher-dimensional embeddings that preserve data topology. These structure-preserving embeddings often yield better results with density-based methods like HDBSCAN compared to clustering on the raw high-dimensional data.

How does supervised umap improve class separation in manifold learning?▼

Supervised manifold learning incorporates labels via the y parameter to guide the embedding process. This semi-supervised approach modifies how UMAP learns the non-linear embeddings, explicitly improving class separation for downstream analysis and visualization tasks.

Do I need to scale features before applying manifold learning with UMAP?▼

Feature scaling is required as appropriate preprocessing before applying UMAP. Standardizing your dataset ensures that distance metrics calculate neighborhood structure accurately, preventing high-variance features from dominating the non-linear embedding learning process.

Can I project new samples into an existing embedding without retraining the model?▼

You can project new samples into an existing embedding by applying the transform() method. This requires fitting the manifold mapping first, enabling quick reuse of the learned structure without recalculating the entire dimensionality reduction model.

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