What are the most important UMAP parameters to tune?

The primary parameters are n_neighbors, which balances local vs. global structure, and min_dist, which controls how tightly points are packed in the embedding.

Does this skill support supervised learning?

Yes, the UMAP-learn skill supports supervised and semi-supervised dimensionality reduction by incorporating target labels during the fitting process.

What is UMAP used for in Claude Code?

It allows Claude to help you visualize high-dimensional data and prepare datasets for clustering by reducing dimensions while preserving topological structure.

Is it compatible with scikit-learn?

Yes, UMAP follows scikit-learn conventions and can be used directly in Pipeline objects and with standard fit/transform methods.

How does UMAP compare to t-SNE?

UMAP is generally faster, scales better to large datasets, and preserves more of the global structure of the data compared to t-SNE's focus on local structure.

UMAP Dimensionality Reduction

Name: UMAP Dimensionality Reduction
Author: jimmc414

byjimmc414

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324

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데이터 과학 및 ML

Performs high-performance nonlinear dimensionality reduction for data visualization and clustering preprocessing using the UMAP algorithm.

UMAP (Uniform Manifold Approximation and Projection) is a powerful tool for visualizing high-dimensional data while preserving both local and global structures. This skill enables Claude to implement UMAP embeddings for tasks ranging from exploratory data analysis and 2D/3D visualization to preprocessing for density-based clustering like HDBSCAN. It supports supervised, semi-supervised, and parametric modes, making it an essential component for any scientific discovery or machine learning pipeline where high-dimensional feature spaces need to be condensed without losing topological relationships.

주요 기능

01Fast nonlinear manifold learning for 2D and 3D visualization

02Parametric UMAP integration for neural network-based embeddings

03Preservation of both local and global data structures across scales

04Advanced clustering preprocessing for algorithms like HDBSCAN

05Support for supervised and semi-supervised dimensionality reduction

06324 GitHub stars

사용 사례

01Preprocessing features to improve the performance and speed of density-based clustering

02Visualizing complex high-dimensional datasets in 2D or 3D scatter plots

03Feature engineering for downstream supervised machine learning models in tabular data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jimmc414/kosmos umap-learn

For use in Claude.ai and ChatGPT

Download Skill