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.

UMAP の次元削減。 2D/3D 視覚化、クラスタリング前処理 (HDBSCAN)、教師あり/パラメトリック UMAP、高次元データ用の高速非線形多様体学習。 ソース: ovachiever/droid-tings。

原文を見る