umap-learn

Topics in the source

• UMAP-Learn
• Overview
• Quick Start
• Installation
• Basic Usage
• Typical Workflow
• Parameter Tuning Guide
• nneighbors (default: 15)

UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique for visualization and general non-linear dimensionality reduction. Apply this skill for fast, scalable embeddings that preserve local and global structure, supervised learning, and clustering preprocessing. UMAP follows scikit-learn conventions and can be used as a drop-in replacement for t-SNE or PCA. Critical preprocessing requirement: Always standardize features to comparable scales before applying UMAP to ensure equal weighting across dimensions. UMAP has four primary parameters that control the embedding behavior. Understanding these is crucial for effective usage. Purpose: Balances local versus global structure in the embedding. How it works: Controls the size of the local neighborhood UMAP examines when learning manifold structure. Effects by value: Low values (2-5): Emphasizes fine local detail but may fragment data into disconnected components Medium values (15-20): Balanced view of both local structure and global relationships (recommended starting point) High values (50-200): Prioritizes broad topological structure at the expense of fine-grained details Recommendation: Start with

This is an excerpt. Refer to the complete source and its current requirements.