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umap-learn

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Applies UMAP-learn to nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.

skills/umap-learn/SKILL.md

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# 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 approximate neighborhood structure, supervised learning, and clustering preprocessing.

Before interpreting an embedding, check finite coordinates, neighborhood retention, sensitivity to seeds/parameters, and domain evidence in the original space. UMAP axes, island areas, and gaps between clusters have no calibrated physical or probabilistic meaning. Keep predictive test data outside preprocessing and embedding fits.

## Quick Start

### Installation

Verified on 2026-10-01: **umap-learn 0.5.12** is the current stable release (April 2026). Requires Python 3.9+ and depends on `scikit-learn>=1.6`, `numba`, `pynndescent`, `numpy`, and `scipy`. Pin to a verified release:
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