Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not [PDF]
Geoscientific Model Development, 2022 The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide ...
T. O. Hodson
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Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature [PDF]
Geoscientific Model Development, 2014 Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be
T. Chai, R. R. Draxler
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Voltage root mean square error calculation for solar cell parameter estimation: A novel g-function approach [PDF]
HeliyonThe existing research on estimating solar cell parameters mainly focuses on minimizing the Root-Mean-Square Error (RMSE) between the estimated and measured current values of solar cells (referred to as the RMSEI).
Martin Ćalasan +4 more
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Root Mean Square Error of Neural Spike Train Sequence Matching with Optogenetics [PDF]
GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017 Optogenetics is an emerging field of neuroscience where neurons are genetically modified to express light-sensitive receptors that enable external control over when the neurons fire.
Eckford, Andrew W. +2 more
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Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model. [PDF]
Pattern Recognit, 2014 The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge,
Zollanvari A, Dougherty ER.
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A novel extended Gumbel Type II model with statistical inference and Covid-19 applications
Results in Physics, 2022 Statistical models play an important role in data analysis, and statisticians are constantly looking for new or relatively new statistical models to fit data sets across a wide range of fields.
Showkat Ahmad Lone +3 more
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Theoretical Structure and Applications of a Newly Enhanced Gumbel Type II Model
Mathematics, 2023 Statistical models are vital in data analysis, and researchers are always on the search for potential or the latest statistical models to fit data sets in a variety of domains. To create an improved statistical model, we used a T-X transformation and the
Showkat Ahmad Lone +5 more
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Review of Root-Mean-Square Error Calculation Methods for Large Deployable Mesh Reflectors
International Journal of Aerospace Engineering, 2022 In the design of a large deployable mesh reflector, high surface accuracy is one of ultimate goals since it directly determines overall performance of the reflector. Therefore, evaluation of surface accuracy is needed in many cases of design and analysis
Sichen Yuan
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Forecasting wind speed data by using a combination of ARIMA model with single exponential smoothing [PDF]
, 2021 Wind serves as natural resources as the solution to minimize global warming and has been commonly used to produce electricity. Because of their uncontrollable wind characteristics, wind speed forecasting is considered one of the best challenges in ...
A. Rahman, Nur H. +5 more
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Research on Moisture Content Determination of Puffs using Near Infrared Spectroscopy Technology
Liang you shipin ke-ji, 2022 Rapid determination of moisture content is an important requirement to ensure the production quality of puffs. In this paper, the NIR spectra of 130 modeling samples and 30 validation samples were collected, using IAS Online-S100 Near Infrared ...
XU Fu-cheng +3 more
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