Root mean square error is simply the square root of mean squared error.
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The test of realism was done with a root mean square error test.
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Compared with published values, this calculation has a root mean square error of only 3.7 s.
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Forecast errors can be evaluated using a variety of methods namely mean percentage error, root mean squared error, mean absolute percentage error, mean squared error.
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CNNs have been used to assess video quality in an objective way after being manually trained; the resulting system had a very low root mean square error.
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The two most popular measures of accuracy that incorporate the forecast error are the Mean Absolute Error ( MAE ) and the Root Mean Squared Error ( RMSE ).
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When the value being predicted is continuously distributed, the mean squared error, root mean squared error or median absolute deviation could be used to summarize the errors.
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The study found : " With few exceptions, the private sector forecasts are less biased and more accurate in terms of mean absolute error and root mean square error.
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In practice, with roundoff errors of random signs, the root mean square errors of pairwise summation actually grow as O ( \ sqrt { \ log n } ).
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Submitted predictions are scored against the true grades in terms of root mean squared error ( RMSE ), and the goal is to reduce this error as much as possible.
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