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Classification accuracies showed that k value offering the lowest error value varied for participants.
While a lowest error value in K = 8 was identified in ADMIXTURE.
Although the multiplication of image alpha values with all generated dictionaries is quite suitable for determining the lowest error value, this process is time consuming.
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The STA training data produced the lowest error values.
The models are compared based on their ability to train with lowest error values.
On the basis of the lowest error values MPSD, Brouser Weron Sototlongo kinetic model indicates better fit with the experimental data as compared to the other models.
Low RMSE and ME for all the interpolation methods indicated their applicability to predict water table depth, and the superiority of UK over all other methods was also well established from its lowest error values.
The model produced the lowest error values (root-mean-square error (RMSE) and percentage error) for winter wheat (RMSE = 1.6 t and 10.1%), sorghum (RMSE = 1.0 t and 5.9%), winter barley (RMSE = 1.8 t and 11.0%), winter rye (RMSE = 1.9 t and 10.4%).
Brouers Weron Sotolongo model (Fig. 2) exhibited the best fit among all the models in case of As V) in terms of the lowest error values (SSE, reduced χ 2 and root-MSE) and the highest correlation coefficients (R, R 2 and (overline{{R^{2} }})).
We are interested in identifying critical residues with the highest specificity and lowest error values, even if the sensitivity is low.
Model desirability approaching unity and with low error value portrays the applicability of the model towards the responses.
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