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This topic has appeared in the trending rankings 1 time(s) in the past year. While it does not trend frequently, its appearance suggests a renewed or concentrated surge of public interest.
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Mean_absolute_error entered the ranking for the first time today at position #. This is its highest position ever recorded.
This topic has appeared in the English Wikipedia rankings 1 time. It first appeared on 2026-04-14 and was most recently seen on 2026-04-14.
In statistics, mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement. MAE is calculated as the sum of absolute errors divided by the sample size:It is thus the arithmetic mean of the absolute errors , where is the prediction and the true value. Alternative formulations may include relative frequencies as weight factors. The mean absolute error uses the same scale as the data being measured. This is known as a scale-dependent accuracy measure and therefore cannot be used to make comparisons between predicted values that use different scales. The mean absolute error is a common measure of forecast error in time series analysis, sometimes used in confusion with the more standard definition of mean absolute deviation. The same confusion exists more generally in technical literature.
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