skills.rest
Ritabrata-Chakraborty
Ritabrata-ChakrabortyCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

umap-learn

Reduce high-dimensional data to 2D or 3D embeddings using UMAP.

Updated Jun 6, 2026
Search Tags:#data visualization#data science#UMAP#dimensionality reduction#clustering
One-click install
npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill umap-learn-ritabrata-chakraborty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/Ritabrata-Chakraborty/Claude-Setup/tree/main/skills/umap-learn
Command: npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill umap-learn-ritabrata-chakraborty

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires umap-learn, numpy, pandas, scikit-learn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

UMAP-learn helps you simplify the process of visualizing high-dimensional data in 2D or 3D spaces, enabling better understanding of data structure and clustering.

Core Features & Use Cases

  • Visualization: Transform high-dimensional data into 2D/3D for intuitive analysis and plotting.
  • Clustering: Prepare data for clustering using HDBSCAN or other density-based algorithms.
  • Supervised Learning: Integrate label information to guide embedding and enhance class separation.
  • Parametric UMAP: Learn and transform data with a neural network-based approach.
  • Use Case: Visualize customer behavior data across multiple features to identify patterns and trends.

Quick Start

Execute the script to perform UMAP reduction on the 'customer_data.csv' file.

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 using UMAP?▼

You can perform dimensionality reduction for clustering by using this Skill to transform data before applying density-based algorithms. It prepares your data structure so clustering methods can better identify meaningful groupings.

Can I use UMAP for supervised learning with labeled data?▼

Yes, UMAP supports supervised learning by integrating label information to guide embedding. This enhances class separation within the transformed space, improving the distinction between different data categories.

What is Parametric UMAP and how does it transform data?▼

Parametric UMAP uses a neural network-based approach to learn and transform data. It provides advanced transformations that allow you to map new data points into an existing learned embedding space.

Do I need numpy and pandas to run UMAP dimensionality reduction?▼

Yes, this Skill utilizes the umap-learn library and requires numpy, pandas, and scikit-learn as dependencies. These libraries provide the foundational array handling and data manipulation needed for the reduction process.

Related Skills

Explore More Data & Analytics Skills→
jaechang-hitsjaechang-hits
298

scikit-learn-machine-learning

Perform classification, regression, clustering, and dimensionality reduction with scikit-learn.

Community
Intermediate
jackspacejackspace
16

umap-learn

Reduce high-dimensional data to low-dimensional embeddings for visualization and clustering.

Community
Advanced
SciMate-AISciMate-AI
1

scikit-learn

Train and evaluate ML models using scikit-learn pipelines.

Official
Advanced
snoodleboot-iosnoodleboot-io
2

dimensionality-reduction

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

Official
Intermediate