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gabrielvuksani
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umap-learn

Reduce high-dimensional datasets into low-dimensional embeddings for visualization and feature extraction.

Updated Aug 23, 2026
Search Tags:#python#embeddings#visualization#dimensionality reduction#umap#clustering preprocessing#supervised embedding
One-click install
npx skills add https://github.com/gabrielvuksani/wotann --skill umap-learn-gabrielvuksani
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/gabrielvuksani/wotann/tree/main/skills/scientific/umap-learn
Command: npx skills add https://github.com/gabrielvuksani/wotann --skill umap-learn-gabrielvuksani

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

UMAP-Learn solves the problem of producing low-dimensional embeddings from high-dimensional data so you can visualize structure and prepare features for downstream learning.

Core Features & Use Cases

  • Fast nonlinear dimensionality reduction: Generate 2D/3D embeddings that preserve local and broader manifold structure for exploratory analysis.
  • Scalable embedding for ML workflows: Use higher-dimensional embeddings (e.g., 5–10+) as preprocessing or feature engineering for clustering and classifiers.
  • Supervised and semi-supervised embedding: Incorporate labels via y to improve class separation, including partial-label scenarios.
  • Clustering preprocessing with HDBSCAN-ready embeddings: Improve density-based clustering by reducing dimensionality while keeping neighborhood relationships.
  • Practical extensions: Support transform() for projecting new data, plus parametric UMAP for efficient learned mappings when you need to reuse the embedding function.

Quick Start

Standardize your features first and then fit a UMAP reducer to produce a 2D embedding suitable for immediate plotting, clustering, or sanity checks.

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 and clustering preprocessing?▼

Dimensionality reduction transforms high-dimensional datasets into fast 2D or 3D embeddings for visualization and clustering preprocessing. You fit a reducer using scikit-learn-style syntax after standardizing your input features to preserve local manifold structure.

What is the best way to project new samples into an existing embedding?▼

Projecting new samples into an existing embedding uses the transform method or parametric UMAP. This applies a learned mapping to efficiently place new data points into the previously generated low-dimensional space without refitting the entire model.

Can I use supervised labels to improve class separation in dimensionality reduction?▼

Supervised dimensionality reduction incorporates labels via the y parameter to improve class separation. This supports both fully labeled datasets and partial-label semi-supervised scenarios to generate embeddings tailored for downstream classification tasks.

Does UMAP work with HDBSCAN for density-based clustering?▼

UMAP works with HDBSCAN by generating higher-dimensional embeddings that reduce noise while keeping neighborhood relationships intact. This preprocessing step improves density-based clustering performance on complex high-dimensional datasets.

What are the limitations of using nonlinear embeddings for feature extraction?▼

Nonlinear embeddings for feature extraction require proper input scaling and configurable neighborhood distance parameters to avoid distorting manifold structure. You must tune these parameters carefully, as default settings may not preserve broader global data relationships.

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