mean absolute error vs mean absolute error of the prediction model

Both phrases are correct, but they are used in different contexts. 'Mean absolute error' is a common term in statistics and machine learning to measure the average magnitude of errors in a set of predictions. On the other hand, 'mean absolute error of the prediction model' specifies that the error is related to a specific model. The choice between the two depends on the level of detail and specificity needed in the context.

Last updated: March 17, 2024 • 644 views

mean absolute error

This phrase is correct and commonly used in statistics and machine learning.

It refers to the average magnitude of errors in a set of predictions.

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In statistics, the mean absolute error (MAE) is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. The mean absolute ...
See also[edit]. Least absolute deviations · Mean absolute error · Mean percentage error · Symmetric mean absolute percentage error ...
Mean absolute error (MAE). The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures ...
Dec 13, 2012 ... Forecast Accuracy: Mean Absolute Error (MAE). Ed Dansereau. Subscribe SubscribedUnsubscribe 505505. Loading... Loading... Working.

mean absolute error of the prediction model

This phrase is correct and provides more specific information by referring to the error of a particular prediction model.

It specifies the average magnitude of errors in the predictions made by a particular model.

Show examples from the web [+]

mean absolute error of the prediction model varies from 0.4 to 1.8 inches in length and 0.3 to 0.8 inch in width at the defect cross-sectional size at the midpoint ...
Oct 12, 2012 ... where MAEPi MAE P i is the mean absolute error of the prediction model Pi P i and. View the MathML source. MAE ¯ P 0 is the mean value of a ...

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mean absolute error vs mean absolute error of the prediction model