Machine Learning & Training

What Is Dimensionality Reduction?

Dimensionality reduction is the process of reducing the number of input variables in a dataset while retaining as much meaningful information as possible. It can improve computational efficiency, reduce noise, and help visualize high-dimensional data. Common methods include principal component analysis, t-SNE, and UMAP.