Principal Component Analysis (PCA) is a dimensionality reduction technique that projects the data into a lower dimensional space called the _______.

  • eigen space
  • feature space
  • subspace
  • variance space
PCA is a technique that projects the data into a new, lower-dimensional subspace. This subspace consists of principal components which are orthogonal to each other and capture the maximum variance in the data.
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