umap-learn
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
npx skills add majiayu000/claude-skill-registry --skill umap-learn-hxk622-tokendance --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# UMAP-Learn ## Overview 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. ## Quick Start ### Installation ```bash uv pip install umap-learn ``` ### Basic Usage UMAP follows scikit-learn conventions and can be used as a drop-in replacement for t-SNE or PCA. ```python import umap from sklearn.preprocessing import StandardScaler # Prepare data (standardization is essential) scaled_data = StandardScaler().fit_transform(data) # Method 1: Single step (fit and transform) embedding = umap.UMAP().fit_transform(scaled_data) # Method 2: Separate steps (for reusing trained model) reducer = umap.UMAP(random_state=42) reducer.fit(scaled_data) embedding = reducer.embedding_ # Access the trained embedding ``` **Critical preprocessing requirement:** Always standardize features to comparable scales before applying UMAP to ensure equal weighting across dimensions. ### Typical Workflow ```python import umap import matplotlib.pyplot as plt from sklearn.pre
- Overview
- Quick Start
- Installation
- Basic Usage
- Typical Workflow
- Parameter Tuning Guide
- nneighbors (default: 15)
- mindist (default: 0.1)
- ncomponents (default: 2)
- metric (default: 'euclidean')
- Parameter Tuning Example
- Supervised and Semi-Supervised Dimension Reduction
- Supervised UMAP
- Semi-Supervised UMAP
uv pip install umap-learn uv pip install umap-learn[parametric_umap] Requires TensorFlow 2.x
What does the umap-learn skill do?
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill umap-learn-hxk622-tokendance --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.