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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Journal of Agricultural Engineering, 2019 Estimation of reference evapotranspiration (ET0) with the Food and Agricultural Organisation (FAO) Penman-Monteith model requires temperature, relative humidity, solar radiation, and wind speed data.
Homayoon Ganji, Takamitsu Kajisa
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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 (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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Correcting the Bias of the Root Mean Squared Error of Approximation under Missing Data [PDF]
Methodology, 2018 Missing data are ubiquitous in both small and large datasets. Missing data may come about as a result of coding or computer error, participant absences, or it may be intentional, as in planned missing designs. We discuss missing data as it relates to goodness-of-fit indices in Structural Equation Modeling (SEM), specifically the effects of missing data
Cailey E. Fitzgerald +4 more
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Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance [PDF]
Climate Research, 2005 The relative abilities of 2, dimensioned statistics — the root-mean-square error (RMSE) and the mean absolute error (MAE) — to describe average model-performance error are examined. The RMSE is of special interest because it is widely reported in the climatic and environmental liter- ature; nevertheless, it is an inappropriate and misinterpreted ...
C. J. Willmott, Kenji Matsuura
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Root mean square error (RMSE) or mean absolute error (MAE)? [PDF]
, 2014 Abstract. 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 a misleading indicator of average error and thus the MAE would be a better metric for that ...
Tianfeng Chai, Roland R. Draxler
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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.
europepmc +6 more sources
“Smart agriculture: a climate-driven approach to modelling and forecasting fall armyworm populations in maize using machine learning algorithms” [PDF]
Frontiers in Plant ScienceThe fall armyworm (Spodoptera frugiperda) poses a significant threat to global maize production owing to its rapid life cycle, extensive host range, and strong dispersal capabilities. We developed a forecasting system for fall armyworm outbreaks over one
Vani Sree Kalisetti +9 more
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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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