by Michele Laurelli
Techniques to reduce the number of features in data while preserving important information.
Dimensionality reduction simplifies data by projecting it to lower dimensions. Benefits include faster computation, reduced storage, and better visualization. Common methods: PCA, t-SNE, UMAP.
PCA for visualization
Feature compression
Noise reduction