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umap-learn

Perform nonlinear dimensionality reduction and manifold learning with UMAP.

1|Updated Jan 14, 2026
Search Tags:#machine learning#feature engineering#visualization#dimensionality reduction#clustering#manifold learning
One-click install
npx skills add https://github.com/Sologa/codex-pipeline --skill umap-learn-sologa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/umap-learn
Command: npx skills add https://github.com/Sologa/codex-pipeline --skill umap-learn-sologa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of understanding and visualizing high-dimensional data by reducing its dimensionality while preserving its essential structure.

Core Features & Use Cases

  • Nonlinear Dimensionality Reduction: Embeds high-dimensional data into lower dimensions (typically 2D or 3D) for visualization.
  • Clustering Preprocessing: Creates effective low-dimensional representations that improve the performance of clustering algorithms like HDBSCAN.
  • Supervised & Semi-Supervised Learning: Incorporates label information to guide the embedding process, aiding in class separation.
  • Feature Engineering: Generates lower-dimensional features for downstream machine learning models.
  • Use Case: Visualize complex biological data, cluster customer segments, or preprocess image features for a classification task.

Quick Start

Use the umap-learn skill to create a 2D embedding of the provided data.

Frequently Asked Questions about umap-learn

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

FAQPage Schema
How does nonlinear dimensionality reduction preserve structure for high-dimensional data visualization?▼

Nonlinear dimensionality reduction preserves structure by using Uniform Manifold Approximation and Projection to embed high-dimensional data into lower dimensions while maintaining its essential topological relationships.

Can I use manifold learning as clustering preprocessing for high-dimensional datasets?▼

Manifold learning serves as clustering preprocessing by creating effective low-dimensional representations that improve the performance of clustering algorithms like HDBSCAN on high-dimensional datasets.

How do I incorporate label information into dimensionality reduction for supervised learning?▼

You can incorporate label information into dimensionality reduction through supervised and semi-supervised parameter tuning, which guides the embedding process and aids in class separation.

What's the best way to generate lower-dimensional features for downstream machine learning models?▼

Feature engineering via manifold learning generates lower-dimensional features from high-dimensional datasets, providing refined inputs for downstream machine learning classification tasks.

Does UMAP support advanced workflows like Parametric UMAP and AlignedUMAP for complex data?▼

UMAP supports advanced workflows through Parametric UMAP and AlignedUMAP features, enabling complex nonlinear dimensionality reduction and manifold learning for sophisticated data processing.

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