UMAP Dimensionality Reduction

About

This skill empowers Claude to perform advanced dimensionality reduction using the UMAP (Uniform Manifold Approximation and Projection) algorithm. It provides a robust framework for projecting high-dimensional datasets into lower dimensions while preserving both local and global structures. Whether you are preparing data for HDBSCAN clustering, creating 2D/3D visualizations of complex manifolds, or performing supervised feature engineering, this skill offers optimized workflows, parameter tuning guidance, and seamless integration with the scikit-learn ecosystem for scalable data science tasks.

Key Features

• Parametric UMAP support using neural network mapping functions
• Visualization support for 2D and 3D embedding spaces
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• Fast and scalable nonlinear dimensionality reduction
• Supervised and semi-supervised learning capabilities for labeled data
• Preprocessing optimization for density-based clustering like HDBSCAN

Use Cases

• Feature engineering for downstream machine learning pipelines
• Visualizing high-dimensional genomic, transcriptomic, or document embeddings
• Preprocessing features to improve density-based clustering performance