Dimensionality reduction is the process of reducing the number of random variables (features) under consideration, typically obtaining a more compact representation of the data while retaining most of the relevant information. This can be done through feature selection (choosing a subset of existing features) or feature extraction (creating new features as combinations of the original ones). Techniques like Principal Component Analysis (PCA), t-SNE, and UMAP transform data from a high-dimensional space into a lower-dimensional space (2D or 3D for visualization, or just fewer dimensions for modeling). By removing redundant or noisy features, dimensionality reduction can alleviate the “curse of dimensionality”, reduce overfitting, and improve model training speed. In essence, it tries to preserve the structure or variance of data in a compressed form.