{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/2"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering"}],"enrichment":{"capability":"UMAP reduces high-dimensional data to lower dimensions for visualization and general non-linear dimensionality reduction, using a manifold-learning approach that preserves both local and global structure.","skillfed_tags":["dimensionality-reduction","manifold-learning","visualization"],"use_cases":["Visualize high-dimensional datasets (e.g., image pixels, gene expression) in 2D or 3D for exploratory data analysis","Preprocess data before clustering to improve cluster quality","Reduce dimensionality as a feature engineering step before training downstream models","Preserve local density information in embeddings using densMAP for single-cell transcriptomics or similar applications","Handle sparse, very high-dimensional data where alternatives become computationally prohibitive"],"what_it_does":"UMAP is a dimension reduction algorithm that embeds high-dimensional data into lower-dimensional space while preserving manifold structure. It models data as lying on a Riemannian manifold and uses fuzzy topological structure to find embeddings that balance local neighborhood relationships with global structure. The package follows a familiar transformer API, integrating naturally into existing ML pipelines.\n\nThe algorithm depends on numpy, scipy, scikit-learn, numba (for performance), pynndescent (for nearest-neighbor search), and tqdm (for progress reporting). It supports sparse matrices, custom distance metrics, and includes densMAP for density-preserving embeddings. You can use it for exploratory visualization, as a preprocessing step before clustering, or as a general-purpose non-linear dimensionality reduction technique.","worth_installing":"Yes. UMAP is actively maintained, has no known vulnerabilities, low install friction, and a permissive BSD license. It is widely used in scientific and ML workflows. Install it if you need dimension reduction with better global structure preservation, or as a general-purpose manifold learning tool for exploratory analysis and preprocessing."},"id":"umap-learn","links":{"html":"https://skillfed.io/packages/umap-learn","md":"https://skillfed.io/packages/umap-learn.md","pypi":"https://pypi.org/project/umap-learn/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-08","license_spdx":null,"license_treatment":"permissive","name":"umap-learn","python_support":"supports_current","summary":"Uniform Manifold Approximation and Projection"},"popularity":{"monthly_downloads":7491195,"position":1736,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.5.12"}