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About umap-learn

U

umap-learn

Applies UMAP-learn to nonlinear dimensionality reduction, 2D/3D embeddings

umap-learn is an agent skill for UMAP, which stands for Uniform Manifold Approximation and Projection, a technique for non-linear dimensionality reduction. It is used for visualisation, for fast and scalable embeddings that preserve neighbourhood structure, for supervised and semi-supervised embeddings, and for preprocessing before clustering. It also covers variants such as DensMAP and AlignedUMAP. The skill tells you what to check before interpreting an embedding: that the coordinates are finite, that neighbourhoods are retained, that results are not overly sensitive to seeds and parameters, and that the domain evidence in the original space agrees. It warns that UMAP axes, island areas and gaps between clusters carry no calibrated physical or probabilistic meaning. It also says to keep predictive test data out of both preprocessing and the embedding fit. The current stable release, umap-learn 0.5.12, was verified on 1 October 2026. It needs Python 3.9 or newer, and depends on scikit-learn 1.6 or newer, numba, pynndescent, NumPy and SciPy. Pin the version for reproducible work.

View umap-learnAll AI Skills apps
Price
Free
Made by
k-dense-ai
Category
AI Skills
Website
github.com/k-dense-ai/scientific-agent-skills/umap-learn