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Four data points have actual values 3, 5, 2 and 6, and the model predicts 2, 5, 4 and 4 for them. What is the mean squared error (MSE)?

  1. A1.25
  2. B2.25
  3. C9
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Answer: B. 2.25

The errors (actual value − predicted value) are 1, 0, −2 and 2. The MSE is the mean of the squared errors: (1 + 0 + 4 + 4) ÷ 4 = 2.25. The mean absolute error (MAE), the mean of the absolute errors, is (1 + 0 + 2 + 2) ÷ 4 = 1.25. Because the MSE squares the errors, it is affected more strongly than the MAE by data points that are far off (outliers).

  • A. This is the mean absolute error (MAE).
  • B. Correct. (1 + 0 + 4 + 4) ÷ 4 = 2.25.
  • C. This is the sum of the squared errors, not divided by the number of data points, 4.
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