# umap-learn

> UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, fo

- Source: https://doutorai.pt/en/components/skills__umap-learn
- Type: Skills
- Category: Scientific
- Downloads: 9
- Site: Doutor AI (https://doutorai.pt/en) - AI tools for small businesses

An open-source community component, curated and adapted by Doutor AI.

## What it does and how to use it

A **skill** that teaches Claude Code to do scientific tasks well. It is packaged knowledge the model uses whenever it is relevant.

1. Install the skill (command above).
2. In Claude Code, describe what you need - the skill turns on by itself when the context fits.
3. No commands to memorize: you talk in plain language and the skill does the rest.

## Install (Claude Code)

```bash
mkdir -p .claude/skills/umap-learn && curl -fsSL https://doutorai.pt/data/lib/skills__umap-learn.md -o .claude/skills/umap-learn/SKILL.md
```

Paste this command into the terminal, in your project folder, and the component installs automatically.

## Content

````
---
name: umap-learn
description: "UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data."
---

# UMAP-Learn

## Overview

UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique for visualization and general non-linear dimensionality reduction. Apply this skill for fast, scalable embeddings that preserve local and global structure, supervised learning, and clustering preprocessing.

## Quick Start

### Installation

```bash
uv pip in

- 🔒 Full content on the Pro plan (€7.99/month) -
````

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## Save hours every week with AI, no hassle

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---
Content by Doutor AI (https://doutorai.pt/en). When quoting, please cite the source: https://doutorai.pt/en/components/skills__umap-learn