Dimensionality reduction is a crucial technique in machine learning and data analysis that aims to reduce the number of random variables under consideration and avoid The Curse of Dimensionality by obtaining a set of principal variables. This process helps in simplifying models, reducing computational costs, and improving the interpretability of the data. Here are some key aspects and techniques of dimensionality reduction.
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