Use UMAP-learn for nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.
---
name: umap-learn
description: Use UMAP-learn for nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.
license: BSD-3-Clause license
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---
# 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
Current stable release: **umap-learn 0.5.12** (released April 2026). Requires Python 3.9+ and depends on `scikit-learn>=1.6`, `numba`, `pynndescent`, `numpy`, and `scipy`. Pin to a verified release:
```bash
uv pip install umap-learn==0.5.12
```
### Basic Usage
… load the full skill through Skill Me