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AI-900 · Question #51

________________ is the average of absolute differences between prediction and actual observation where all individual differences have equal weight. Select the appropriate regression performance metr

The correct answer is D. Mean Absolute Error (MAE). Mean Absolute Error (MAE) is a regression metric that calculates the average of the absolute differences between predicted and actual values. It assigns equal weight to all individual errors, providing a clear measure of prediction accuracy.

Submitted by krish.m· Mar 30, 2026Describe fundamental principles of machine learning on Azure

Question

________________ is the average of absolute differences between prediction and actual observation where all individual differences have equal weight. Select the appropriate regression performance metrics from below to complete the sentence.

Options

  • ACoefficient of Determination (R2)
  • BRelative Squared Error (RSE)
  • CRelative Absolute Error (RAE)
  • DMean Absolute Error (MAE)

How the community answered

(32 responses)
  • A
    3% (1)
  • B
    3% (1)
  • D
    94% (30)

Why each option

Mean Absolute Error (MAE) is a regression metric that calculates the average of the absolute differences between predicted and actual values. It assigns equal weight to all individual errors, providing a clear measure of prediction accuracy.

ACoefficient of Determination (R2)

Coefficient of Determination (R2) measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s) and is not based on absolute differences.

BRelative Squared Error (RSE)

Relative Squared Error (RSE) normalizes the total squared error by the total squared error of a simple predictor (e.g., mean), making it a relative measure, not the average of absolute differences.

CRelative Absolute Error (RAE)

Relative Absolute Error (RAE) normalizes the total absolute error by the total absolute error of a simple predictor (e.g., mean), and while it uses absolute errors, it is a relative measure, not simply the average of absolute differences.

DMean Absolute Error (MAE)Correct

Mean Absolute Error (MAE) is defined as the average of the absolute differences between the predicted values and the actual observed values. This metric ensures that all individual errors contribute equally to the overall score, fulfilling the description of 'average of absolute differences' with 'equal weight'.

Concept tested: Regression performance metrics

Source: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-track-models?view=azureml-api-2#supported-metrics

Topics

#Regression metrics#Mean Absolute Error#MAE

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