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

•

324

Data Science & ML

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

About

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.

Key Features

Fast nonlinear manifold learning for 2D and 3D visualization
Parametric UMAP integration for neural network-based embeddings
Preservation of both local and global data structures across scales
Advanced clustering preprocessing for algorithms like HDBSCAN
Support for supervised and semi-supervised dimensionality reduction
324 GitHub stars

Use Cases

Preprocessing features to improve the performance and speed of density-based clustering
Visualizing complex high-dimensional datasets in 2D or 3D scatter plots
Feature engineering for downstream supervised machine learning models in tabular data

About

Key Features

Fast nonlinear manifold learning for 2D and 3D visualization
Parametric UMAP integration for neural network-based embeddings
Preservation of both local and global data structures across scales
Advanced clustering preprocessing for algorithms like HDBSCAN
Support for supervised and semi-supervised dimensionality reduction
324 GitHub stars

Use Cases

Preprocessing features to improve the performance and speed of density-based clustering
Visualizing complex high-dimensional datasets in 2D or 3D scatter plots
Feature engineering for downstream supervised machine learning models in tabular data