Can UMAP handle new data after the initial training?

Yes, UMAP includes a transform() method to project new points into the existing embedding space, and the skill also covers Parametric UMAP for even more efficient neural-network-based transformations.

Does this skill help with clustering high-dimensional data?

Yes, it provides specific 'Best Practices for Clustering' which include using 5-10 dimensions and setting min_dist to 0.0 to improve density-based clustering results with algorithms like HDBSCAN.

What are the most important parameters to tune in UMAP?

The two most critical parameters are n_neighbors (balancing local vs. global structure) and min_dist (controlling how tightly points are allowed to pack together).

Why use UMAP over t-SNE in Claude Code?

UMAP is generally faster, scales better to large datasets, and preserves more of the global structure of the data compared to t-SNE, making it more effective for both visualization and clustering.

UMAP Machine Learning

Name: UMAP Machine Learning
Author: K-Dense-AI

byK-Dense-AI

•

2,188

•

Data Science & ML

Performs fast non-linear dimensionality reduction and manifold learning for data visualization and clustering preprocessing.

The UMAP Machine Learning skill provides Claude with specialized knowledge to implement the Uniform Manifold Approximation and Projection (UMAP) algorithm. It facilitates high-dimensional data analysis by preserving both local and global structures better than traditional methods like t-SNE. This skill is essential for data scientists looking to visualize complex datasets, prepare data for density-based clustering like HDBSCAN, or perform supervised metric learning to improve downstream machine learning model performance.

Key Features

01Integration patterns for scikit-learn compatible ML pipelines

022,188 GitHub stars

03Optimized preprocessing configurations for HDBSCAN clustering

04Support for Parametric UMAP using neural network architectures

05High-speed non-linear dimensionality reduction for 2D/3D visualization

06Supervised and semi-supervised manifold learning capabilities

Use Cases

01Feature engineering for supervised classification tasks on complex manifolds

02Visualizing high-dimensional genomic, transcriptomic, or document embeddings

03Preprocessing noisy data to improve cluster discovery and density estimation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add k-dense-ai/claude-scientific-skills umap-learn

For use in Claude.ai and ChatGPT

Download Skill