Dimensionality Reduction

Dimensionality reduction is a technique commonly used in statistical learning and data analysis to reduce the number of random variables being evaluated. It uses principal component analysis (PCA) to reduce models while preserving important data. This method is very useful when dealing with large datasets with multiple variables.

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