Reduce high-dimensional data into low-dimensional embeddings with UMAP.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill umap-learn-jasrajtulsi
Please help me install this Agent Skill. Skill: umap-learn Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/umap-learn Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill umap-learn-jasrajtulsi
UMAP-Learn helps you turn high-dimensional data into compact, meaningful embeddings so you can visualize structure, compare samples, and prepare features without losing important relationships.
Use the umap-learn skill to embed my dataset into two dimensions with metric-aware preprocessing and reproducible settings.
High-intent search queries and answers about installing and using this skill.
You can reduce high-dimensional data using UMAP to project it into 2D or 3D embeddings for visualization and clustering. This preserves local and global structure better than linear methods across numeric, text, and temporal datasets.
UMAP supports supervised and semi-supervised workflows by using labels to guide the embedding process. This yields clearer class separation for fully or partially labeled datasets.
AlignedUMAP is designed for embedding related time-series or batch datasets consistently. It aligns embeddings across multiple datasets to maintain comparable structures over time or between batches.
UMAP provides sklearn-compatible fit, transform, and inverse_transform methods for seamless pipeline integration. It requires metric-aware preprocessing and reproducible random_state control for reliable execution.
Parametric UMAP learns a transform function that can be applied to project new, unseen data. This allows you to map incoming data points into the existing low-dimensional embedding space without retraining.
Use DensMAP instead of standard UMAP when your analysis requires preserving the local density of data points in the low-dimensional embedding. This helps accurately represent clusters with varying densities.
LeonChaoXGenerate UMAP dimensionality-reduced embeddings for visualization and clustering workflows.
qinyan-aiReduce high-dimensional data to low-dimensional embeddings with a sklearn-compatible API.
dralkhReduce high-dimensional data into low-dimensional manifold embeddings with UMAP.
yf8578Reduce high-dimensional datasets to low-dimensional embeddings using UMAP.
