umap-learn - UMAP dimensionality reduction and embeddings
Apply UMAP for nonlinear dimensionality reduction, visualization, supervised embeddings, new-data transformation, and clustering preprocessing.
Tags
Updated: 2026-09-29Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Reduce dimensionality
- Create low-dimensional embeddings
- Fit supervised embeddings
- Transform new data
- Prepare data for clustering
- Apply custom distance metrics
Inputs
- High-dimensional feature data
- Optional target labels
- UMAP parameters
- New data for transformation
Outputs
- Low-dimensional embeddings
- Transformed data embeddings
- Fitted UMAP reducer
Requirements
- Python environment
- umap-learn package