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LeonChaoX
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umap-learn

Generate UMAP dimensionality-reduced embeddings for visualization and clustering workflows.

783|65|Updated Feb 27, 2026
Search Tags:#embeddings#visualization#dimensionality reduction#umap#hdbscan#clustering preprocessing#supervised umap
One-click install
npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill umap-learn-leonchaox
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/LeonChaoX/qinyan-academic-skills/tree/main/skills/09-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B8%8E%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/umap-learn
Command: npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill umap-learn-leonchaox

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you reduce high-dimensional data into low-dimensional embeddings so you can visualize structure, preprocess for clustering, and improve downstream machine learning.

Core Features & Use Cases

  • Dimensionality Reduction for Visualization & Modeling: Create fast nonlinear embeddings with UMAP that preserve local and global relationships for 2D/3D plots and feature engineering.
  • Clustering-Ready Preprocessing: Tune UMAP parameters to generate embeddings that work well with density-based clustering such as HDBSCAN.
  • Supervised / Semi-Supervised Embedding: Use label information (y) to separate classes, including partial-label scenarios via -1 for unlabeled samples.

Quick Start

Train an embedding model using standardized features, then scatter-plot the 2D result with cluster labels to validate separability for your dataset.

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 2D or 3D visualization?▼

Dimensionality reduction with UMAP transforms high-dimensional datasets into low-dimensional embeddings, preserving local and global relationships for fast 2D or 3D scatter plots. Inputs must be standardized numeric feature sets.

Can I use UMAP for clustering preprocessing with HDBSCAN?▼

Yes, UMAP serves as clustering preprocessing by tuning parameters to generate embeddings optimized for density-based clustering algorithms like HDBSCAN. This prepares high-dimensional data for effective cluster separation.

How does supervised UMAP work for separating labeled classes?▼

Supervised UMAP uses label information (y) during embedding to separate classes, while semi-supervised modes handle partial-label scenarios by assigning -1 to unlabeled samples for guided manifold learning.

What are the core UMAP parameters I need to tune for manifold learning?▼

Core UMAP parameters include n_neighbors, min_dist, n_components, and metric. Adjusting these controls the balance between local and global structure preservation in your nonlinear manifold learning embeddings.

Does UMAP support transform and inverse_transform operations?▼

Yes, fitted UMAP models support standard transform and inverse_transform operations, allowing you to project new data into the learned embedding space or reconstruct high-dimensional approximations from low-dimensional coordinates.

What advanced UMAP variants are available for complex datasets?▼

Advanced variants include parametric UMAP and AlignedUMAP, which extend standard manifold learning capabilities for complex datasets like gene expression matrices and other high-dimensional numeric feature sets.

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