Dimensionality Reduction

The process of reducing the number of variables or features in a dataset while preserving as much useful information as possible. It makes data easier to visualize, speeds up training, and can improve model performance by stripping out noise. Common approaches include PCA and UMAP.
See also: embedding, feature engineering, clustering.