Which statement correctly describes Mean Squared Error (MSE)?

Options

  • A. It measures prediction error by calculating the average of the squared differences between actual and predicted values
  • B. It measures only the number of correctly classified classes
  • C. It is used exclusively to construct a confusion matrix
  • D. It ignores the difference between actual and predicted values
  • E. None of the above

Correct Answer (Detailed Explanation is Below)

A. It measures prediction error by calculating the average of the squared differences between actual and predicted values

Detailed Explanation

Mean Squared Error (MSE) is a commonly used evaluation metric for regression models. It is calculated as the average of the squared differences between actual and predicted values. Because the errors are squared, larger errors receive greater weight.

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