Is data standardization necessary before using this skill?

Standardization is critical. You should always scale features to comparable ranges before applying UMAP to ensure all dimensions are weighted equally.

How does UMAP compare to t-SNE for AI tasks?

UMAP is generally faster than t-SNE, preserves more global structure, and offers better scalability, making it ideal for large-scale AI agent data analysis.

What is Parametric UMAP?

Parametric UMAP uses neural networks to learn the mapping function, allowing for efficient transformation of new data and integration with deep learning architectures.

Can I use UMAP for supervised learning?

Yes, UMAP supports both supervised and semi-supervised dimension reduction by incorporating label information during the fitting process to guide the embedding.

What are the core parameters to tune in UMAP?

The primary parameters are n_neighbors (local vs. global balance), min_dist (cluster density), n_components (output dimensions), and metric (distance calculation).

UMAP Machine Learning

Name: UMAP Machine Learning
Author: x-cmd

byx-cmd

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Ciencia de Datos y ML

Performs fast nonlinear dimensionality reduction and manifold learning for high-dimensional data visualization and clustering.

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The umap-learn skill equips Claude Code with specialized knowledge for handling complex, high-dimensional datasets using Uniform Manifold Approximation and Projection. It provides implementation patterns for creating scalable embeddings that preserve both local and global data structures, making it superior to t-SNE for many AI and data science tasks. Use this skill to implement standardized workflows for data visualization, clustering preprocessing with HDBSCAN, and advanced supervised or parametric dimensionality reduction within your Python environment.

Características Principales

8 GitHub stars
Fast and scalable nonlinear dimensionality reduction
Support for supervised and semi-supervised embedding models
Parametric UMAP implementation via neural network mappings
Optimized preprocessing configurations for density-based clustering
Preservation of both local and global topological data structures

Casos de Uso

Preprocessing complex datasets to improve the accuracy and speed of HDBSCAN clustering
Visualizing high-dimensional embeddings (text, images, or biological data) in 2D/3D space
Feature engineering to reduce dimensionality in downstream machine learning pipelines

Acerca de

Características Principales

8 GitHub stars
Fast and scalable nonlinear dimensionality reduction
Support for supervised and semi-supervised embedding models
Parametric UMAP implementation via neural network mappings
Optimized preprocessing configurations for density-based clustering
Preservation of both local and global topological data structures

Casos de Uso

Preprocessing complex datasets to improve the accuracy and speed of HDBSCAN clustering
Visualizing high-dimensional embeddings (text, images, or biological data) in 2D/3D space
Feature engineering to reduce dimensionality in downstream machine learning pipelines