UMAP Dimensionality Reduction

소개

UMAP (Uniform Manifold Approximation and Projection) is a versatile tool for visual data exploration and non-linear dimensionality reduction. This skill provides expert guidance on implementing UMAP workflows, from critical data preprocessing and parameter tuning (n_neighbors, min_dist) to advanced techniques like supervised learning and clustering preprocessing for HDBSCAN. It enables data scientists to preserve both local and global data structures more effectively and efficiently than t-SNE, making it ideal for everything from feature engineering in machine learning pipelines to deep learning integration via Parametric UMAP.

주요 기능

• Parametric UMAP support using neural networks for deep learning workflows
• Fast non-linear dimensionality reduction for 2D/3D data visualization
• Seamless integration with scikit-learn pipelines and transform methods
• Optimized preprocessing for density-based clustering algorithms like HDBSCAN
• Support for supervised and semi-supervised manifold learning with label guidance
• 16 GitHub stars

사용 사례

• Visualizing complex high-dimensional datasets in 2D or 3D space
• Improving clustering accuracy by overcoming the curse of dimensionality
• Feature engineering to reduce input dimensions for downstream ML models