UMAP Dimensionality Reduction

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.

Key Features

Use Cases