What is the basic purpose of Principal Component Analysis (PCA)?

Options

  • A. To reduce the dimensionality of data while retaining as much important variation as possible
  • B. To assign predefined class labels to data
  • C. To divide data into exactly K clusters
  • D. To generate association rules from transaction data
  • E. None of the above

Correct Answer (Detailed Explanation is Below)

A. To reduce the dimensionality of data while retaining as much important variation as possible

Detailed Explanation

Principal Component Analysis (PCA) is a dimensionality reduction technique. It transforms the original features into a smaller set of uncorrelated variables called principal components. The first components capture the largest amount of variance in the data, allowing dimensionality to be reduced while retaining important information.

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