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UMAP-learn

Maintainer K-Dense Inc. · Last updated April 1, 2026

UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique for visuali.

Claude CodeOpenClawNanoClawAnalysisWritingumap-learnmachine-learningpackagemachine learning & deep learning

Original source

K-Dense-AI/claude-scientific-skills

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/umap-learn

Maintainer: K-Dense Inc.
License: BSD-3-Clause license
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

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.
scaled_data = StandardScaler().fit_transform(data).

Source Doc

Excerpt From SKILL.md

Basic Usage

UMAP follows scikit-learn conventions and can be used as a drop-in replacement for t-SNE or PCA.

import umap
from sklearn.preprocessing import StandardScaler

## Method 1: Single step (fit and transform)

embedding = umap.UMAP().fit_transform(scaled_data)

## Method 2: Separate steps (for reusing trained model)

reducer = umap.UMAP(random_state=42)
reducer.fit(scaled_data)
embedding = reducer.embedding_  # Access the trained embedding

Critical preprocessing requirement: Always standardize features to comparable scales before applying UMAP to ensure equal weighting across dimensions.