umap-learn

Name: umap-learn
Availability: InStock
Author: estrella-231

Generate low-dimensional embeddings from high-dimensional datasets with sklearn-style fit/transform workflows.

Updated May 24, 2026

Search Tags:#visualization #dimensionality reduction #umap #hdbscan #clustering preprocessing #supervised embeddings #feature scaling

Ready to run

npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill umap-learn-estrella-231

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/umap-learn
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill umap-learn-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Dimensionality reduction for high-dimensional data is difficult because it can be slow and distort structure, especially when you need embeddings that preserve neighborhood patterns for visualization or clustering.

Core Features & Use Cases

High-dimensional embedding for visualization and ML: Produce low-dimensional representations (2D/3D or higher-dimensional embeddings) that approximate manifold structure for fast inspection and downstream modeling.
Supervised and semi-supervised embeddings: Incorporate label information via supervised UMAP to improve class separation, including partial-label scenarios.
Clustering-friendly preprocessing: Generate embeddings optimized for density-based clustering workflows (e.g., with HDBSCAN) and optionally support transform/reuse for new data.

Quick Start

Use the umap-learn skill to embed your standardized dataset stored in data and return a 2D embedding for plotting by running: "fit_transform UMAP on StandardScaler(data) with n_neighbors=15, min_dist=0.1, n_components=2, metric='euclidean', random_state=42".

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 neighborhood structure?▼

Dimensionality reduction with UMAP generates fast low-dimensional embeddings that preserve meaningful manifold structure, making it ideal for visualizing high-dimensional datasets in 2D or 3D while maintaining local neighborhoods.

What's the best way to preprocess features before applying UMAP embeddings?▼

Prior feature standardization is required before generating UMAP embeddings. You should apply StandardScaler to your tabular numeric data or document vectors to ensure neighborhood distance parameters compute accurately during the fit_transform workflow.

Can I use supervised labels to improve class separation in my embeddings?▼

Supervised UMAP incorporates label information via the target parameter (y) to improve class separation in the resulting embeddings. This supports both fully labeled and partially labeled datasets for semi-supervised learning workflows.

Does UMAP work well as a clustering preprocessing step for HDBSCAN?▼

UMAP serves as clustering-friendly preprocessing for density-based clustering workflows like HDBSCAN. It produces embeddings optimized to preserve local manifold structure, which helps density-based algorithms identify meaningful clusters in high-dimensional data.

What parameters control neighborhood size and distance in UMAP embeddings?▼

The n_neighbors parameter controls local neighborhood size, while min_dist controls minimum distance between embedded points. You can also configure the distance metric (e.g., euclidean) and n_components to customize the embedding output for your specific dataset.

When should I not use UMAP for dimensionality reduction?▼

UMAP may not be suitable when strict preservation of global distance relationships is required, as it prioritizes local neighborhood structure. Additionally, without prior feature standardization, distance calculations on unscaled high-dimensional data can produce distorted embeddings.