skills.rest
Weiwei-Mao
Weiwei-MaoCOMMUNITY
Explore all skills by this author →
GitHub

umap-learn

Reduce high-dimensional datasets to lower dimensions using UMAP manifold learning.

2|Updated Feb 2, 2026
Search Tags:#machine learning#visualization#dimensionality reduction#clustering#umap#manifold learning
One-click install
npx skills add https://github.com/Weiwei-Mao/hydrology-skills --skill umap-learn-weiwei-mao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/Weiwei-Mao/hydrology-skills/tree/main/hydrology-skills/umap-learn
Command: npx skills add https://github.com/Weiwei-Mao/hydrology-skills --skill umap-learn-weiwei-mao

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires umap-learn, scikit-learn, tensorflow, numba, hdbscan, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of visualizing and analyzing high-dimensional data by reducing it to a lower-dimensional space while preserving its essential structure.

Core Features & Use Cases

  • Dimensionality Reduction: Reduces data to 2D or 3D for visualization, or to higher dimensions for feature engineering.
  • Manifold Learning: Captures non-linear relationships in data.
  • Clustering Preprocessing: Prepares data for density-based clustering algorithms like HDBSCAN.
  • Supervised Learning: Incorporates label information to guide the embedding.
  • Use Case: Visualizing complex biological datasets, customer segmentation, or anomaly detection in high-dimensional sensor data.

Quick Start

Use the umap-learn skill to reduce the dimensionality of the provided data to 2 components for visualization.

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 visualization?▼

Dimensionality reduction for visualization is achieved by applying UMAP to transform high-dimensional datasets into 2D or 3D spaces. This preserves essential non-linear manifold structure for graphical plotting.

Can I use UMAP for clustering preprocessing with HDBSCAN?▼

Yes, UMAP prepares high-dimensional data for density-based clustering by reducing dimensions before applying HDBSCAN. This workflow improves clustering performance on complex non-linear datasets.

Does UMAP support supervised learning and label information?▼

UMAP supports supervised learning by incorporating label information to guide the manifold embedding. This allows the dimensionality reduction to respect known class boundaries during transformation.

What is the best way to visualize complex non-linear relationships in datasets?▼

Manifold learning via UMAP captures non-linear relationships in high-dimensional data by projecting them into lower dimensions. It provides fast, scalable visualizations for complex biological or sensor datasets.

Can I use Parametric UMAP and AlignedUMAP for complex machine learning workflows?▼

Parametric UMAP and AlignedUMAP are supported for complex workflows. Parametric UMAP uses TensorFlow for deep learning integration, while AlignedUMAP handles sequential data alignment across models.

What distance metrics does UMAP support for anomaly detection?▼

UMAP supports various distance metrics for anomaly detection in high-dimensional sensor data. These metrics allow flexible calculation of manifold distances to identify outliers during dimensionality reduction.

Related Skills

Explore More Data & Analytics Skills→
yf8578yf8578
1

umap-learn

Reduce high-dimensional datasets to low-dimensional embeddings using UMAP.

Community
Intermediate
CRAG666CRAG666
46

scikit-learn

Train and evaluate scikit-learn classification and regression models on structured data.

Community
Advanced
HolobiomicsLabHolobiomicsLab
4

umap-learn

Compute low-dimensional embeddings from high-dimensional data using UMAP.

Official
Advanced
snoodleboot-iosnoodleboot-io
2

dimensionality-reduction

Reduces dataset feature dimensions using PCA or t-SNE for ML preprocessing.

Official
Intermediate