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Exact(10)
However, the maximum error value for this model is less than 3%.
We assume an explicit error value for each component and for each event, as given in Table 1.
Setting a large error value for a certain station's data is equivalent to applying a small weighting to it in the sum.
In an off-line evaluation of results, both models show good predicting abilities; in particular the ANFIS model presents an absolute error value for the training and test phases respectively equal to 0.7 and 0.9 (as a percentage of 3.5% and 4.5%), while, the same evaluation obtained using the ANN-BP model provides 0.92 and 0.9 (as a percentage of 4.6% and 4.5%).
To discover the best ANFIS model, having a tolerable error value for both training and test data sets, the training options including radii value must change a great deal to reach a balance point between over-fitting and under-fitting scenarios.
It indicates that it is hard to find an input that produces a high error value for this particular structure, which suggests that such a realization may properly operate most of the time; however, it cannot be guaranteed, since the model checking did not finish.
Similar(49)
Mean relative error values for both error types are calculated by averaging instantaneous relative error values for all channel realizations.
Moreover, the average error values for were higher than that of and.
Generally, the mean error values for both temperature and precipitation were relatively small.
Moreover, random tree classifier shows the highest error values for both feature selection techniques.
For rotation, we see that the error values for - and -axis are all because there is no rotation about these axes.
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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