Dimensional reduction

Dimensional reduction is a process used in data analysis and machine learning to reduce the number of random variables or features in a dataset while preserving its essential information. This is particularly useful when dealing with high-dimensional data, which can be challenging to visualize, analyze, and model due to the "curse of dimensionality" — a phenomenon where the feature space becomes increasingly sparse and less manageable as the number of dimensions increases.