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What does principal component analysis do?

  1. A Projects data onto orthogonal directions of maximum variance to reduce dimensionality
  2. B Selects the most important original features
  3. C Balances class distributions
  4. D Removes outliers
Answer

Projects data onto orthogonal directions of maximum variance to reduce dimensionality

Components are linear combinations, so interpretability is lost, and features must be scaled before applying it.

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