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UMAP-learn Dimensionality Reduction Helper

Guides you through UMAP embeddings, parameter tuning, and clustering preprocessing with ready-to-run scikit-learn code.

Data & AnalyticsIntermediate★ 33,030⑂ 3,248AI score 9/10Last updated: Aug 9, 2026

What it does

  • Gives concrete tuning rules for UMAP's four core parameters (n_neighbors, min_dist, n_components, metric) split by goal: visualization, clustering, or ML feature engineering.
  • Provides working code for supervised/semi-supervised UMAP (y=labels, -1 for unlabeled), HDBSCAN clustering pipelines, transform() on unseen data, inverse transforms, AlignedUMAP, and Parametric UMAP.
  • Includes a troubleshooting table for fragmented clusters, collapsed embeddings, NaN/inf inputs, and local umap.py files shadowing the installed package.
  • Explains the reproducibility vs. throughput trade-off around random_state.

Who it's for

  • Data analysts and scientists doing EDA and visualization in the scikit-learn ecosystem.
  • Researchers with high-dimensional data (scRNA-seq, embedding vectors, document vectors).
  • ML engineers replacing t-SNE with something faster and more scalable.

Example uses

  1. "Reduce this embedding matrix to 2D and plot it colored by label" → standardize, run UMAP(n_neighbors=15, min_dist=0.1), emit matplotlib code.
  2. "I want HDBSCAN clusters but my data is too high-dimensional" → reduce to 10 components with min_dist=0.0, cluster, and score with Adjusted Rand Index.
  3. "Project a new batch into the embedding I already trained" → split fit/transform, or switch to Parametric UMAP when the distribution shifts.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/umap-learn/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/umap-learn folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/umap-learn/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/sas && mkdir -p ~/.claude/skills && cp -r /tmp/sas/skills/umap-learn ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal.
  2. Clone the repo: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Create the skills folder: mkdir -p ~/.claude/skills
  4. Copy just this skill: cp -r scientific-agent-skills/skills/umap-learn ~/.claude/skills/
  5. Install Python deps: pip install umap-learn scikit-learn matplotlib (add pip install hdbscan for clustering). Check PyPI for the actual latest version before pinning one.
  6. Restart Claude Code and ask something like "use UMAP to reduce this dataset to 2D" to trigger the skill.
View source on GitHub ↗License: MIT