umap-learn

SKILL.md

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.

UMAP follows scikit-learn conventions and can be used as a drop-in replacement for t-SNE or PCA.

Critical preprocessing requirement: Always standardize features to comparable scales before applying UMAP to ensure equal weighting across dimensions.

Réduction de dimensionnalité UMAP. Apprentissage multiple non linéaire rapide pour la visualisation 2D/3D, prétraitement de clustering (HDBSCAN), UMAP supervisé/paramétrique, pour les données de grande dimension. Source : ovachiever/droid-tings.

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