umap-learn - UMAP dimensionality reduction and embedding
Applies UMAP for nonlinear dimensionality reduction, visualization, supervised embeddings, new-data transformation, and clustering preprocessing.
Tags
Updated: 2026-09-29dimensionality reductionmanifold learningembeddingsvisualizationsupervised learningclustering preprocessing
Capabilities
What this skill does
- Reduce data dimensions
- Create low-dimensional embeddings
- Guide embeddings with labels
- Transform new data
- Preprocess clustering data
- Tune embedding parameters
- Visualize embeddings
Inputs
- High-dimensional data
- Optional target labels
- UMAP parameters
- New input data
- Clustering configuration
Outputs
- Low-dimensional embeddings
- Transformed data embeddings
- Cluster labels
- Visualization plots
- Evaluation scores
Requirements
- Python environment
- umap-learn package
- scikit-learn package
- HDBSCAN package for clustering
- Matplotlib for plotting