What is the basic principle of the K-Nearest Neighbors (KNN) algorithm?

Options

  • A. A new data point is classified according to the classes of its nearest training examples
  • B. A decision tree is always constructed before classification
  • C. The algorithm assumes that all features are independent
  • D. The algorithm uses only the oldest training example
  • E. None of the above

Correct Answer (Detailed Explanation is Below)

A. A new data point is classified according to the classes of its nearest training examples

Detailed Explanation

K-Nearest Neighbors (KNN) is a supervised learning algorithm that determines the class of a new observation based on its nearest training examples. For classification, the most common class among the selected K neighbors is generally assigned to the new observation.

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