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It was in the same range as the systematic variance induced by the error estimation technique itself during different cross-validation folds.
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The optimum value of refractive index modulation amplitude, Δn, CFBG length, ( {L}_{mathit{mathsf{g}}} ), and the chirp parameter, C, is obtained by using the error estimation equation (Eq. 2) where error, E is defined as E={displaystyle sum_{i=1}^N}{left {R}_{Obj}left({lambda}_iright -{R}_{Sim}left({lambda}_iright -{R}_{}^2=0, i=1,2,dotSim}left.
By Proposition 1, the error estimation is (epsilon=sum_{ineq1}frac{4sin^{2}(lambda_{i}{/2)}{T^{2}lambda_{i}^{3}}|lasglambdarightarrow0lambdarightarrow0+O(lambdarightarrow0lambdarightarrow0).
Secondly we described the stable XFEM step by step, we also gave the error estimation if we use Q-1 element.
end{aligned} We derive the error estimation (4.5) by taking the infimum over (lambda_{h}in M_{h}).
In our implementation [55] we have improved the error estimation strategy by using two-levels of cross-validations.
Their idea was to obtain, in β-normed spaces, the existence of the exact solution and the error estimations by using the fixed point alternative theorem [6].
A generalization of the ML approach including more flexible cost functions is given by the prediction error estimation method ([ 5]).
Previous studies base their verification by the difference in error estimation, whereas this study uses various error estimation methods and design of experiment (DOE).
The approximation error can be controlled by an error estimation.
The relative importance (RI) of a predictor in a Random Forest model is obtained by the out-of-bag (OOB) error estimation, which is the increase of mean squared error (MSE) when the predictor values are permuted.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com