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
doaj +4 more sources
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
doaj +4 more sources
Moments and root-mean-square error of the Bayesian MMSE estimator of classification error in the Gaussian model [PDF]
Pattern Recognition, 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.
Amin Zollanvari
exaly +4 more sources
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
doaj +2 more sources
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
doaj +2 more sources
Generative adversarial network (GAN) and enhanced root mean square error (ERMSE): deep learning for stock price movement prediction [PDF]
Multimedia tools and applications, 2021 The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and
Ashish Kumar +6 more
semanticscholar +1 more source
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
doaj +1 more source
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
doaj +1 more source
Achieving Heisenberg scaling with maximally entangled states: An analytic upper bound for the attainable root-mean-square error [PDF]
Physical Review A, 2020 In this paper we explore the possibility of performing Heisenberg limited quantum metrology of a phase, without any prior, by employing only maximally entangled states. Starting from the estimator introduced by Higgins et al. in New J. Phys.
Federico Belliardo, V. Giovannetti
semanticscholar +1 more source