umap-learn - Reduce high-dimensional data with UMAP
Apply UMAP for nonlinear dimensionality reduction, visualization, supervised embeddings, and clustering preprocessing.
Tags
Updated: 2026-09-29Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Reduce data dimensions
- Create 2D or 3D embeddings
- Tune embedding parameters
- Use supervised labels
- Transform new data
- Prepare clustering embeddings
Inputs
- High-dimensional data
- Target labels
- UMAP parameters
- New data
Outputs
- Low-dimensional embeddings
- Transformed data embeddings
- Cluster labels
- Fitted UMAP reducer
Requirements
- Python environment
- umap-learn package
- scikit-learn for preprocessing