Dimensionality Reduction

High-dimensional datasets can contain many features, some of which may be redundant, irrelevant, or highly correlated. Dimensionality reduction transforms or selects these features to create a smaller representation of the original data. Common techniques include Principal Component Analysis (PCA), feature selection, and methods such as t-SNE for visualization.

Reducing the number of dimensions can make datasets easier to process, analyze, and visualize. It can lower computational requirements, reduce noise, and sometimes improve model performance by removing unnecessary information. It also helps address challenges associated with the "curse of dimensionality."