UMAP Dimensionality Reduction

关于

The UMAP-Learn skill enables Claude to perform advanced non-linear dimensionality reduction on high-dimensional datasets. It provides a scalable and fast alternative to t-SNE that better preserves both local and global data structures. This skill is essential for data scientists needing to visualize complex data in 2D or 3D, preprocess features for density-based clustering like HDBSCAN, and apply supervised or parametric manifold learning using deep learning architectures.

主要功能

• Preservation of both local and global topological data structures
• Parametric UMAP support for deep learning integration and model deployment
• Supervised and semi-supervised dimensionality reduction using label guidance
• Fast, non-linear manifold learning for high-dimensional data visualization
• 81 GitHub stars
• Optimized preprocessing configurations for HDBSCAN clustering

使用场景

• Performing supervised feature engineering to improve class separation in noisy data
• Visualizing high-dimensional biological, text, or financial datasets in 2D/3D space
• Preprocessing complex feature sets for downstream machine learning and clustering pipelines