Concept
Dimensionality reduction is a process used in data analysis and machine learning to reduce the number of random variables under consideration, by obtaining a set of principal variables. This technique helps in mitigating the curse of dimensionality, improving model performance, and visualizing high-dimensional data in a more comprehensible way.
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