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Experiment errors of tests were compared.
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All the experiment performed here was repeated five times, and the relative errors of test data were found to be smaller than 5%.
So began an odyssey of trial and error, of testing designs and materials, and comparing sources.
Decision on the optimum topology was based on the minimum error of testing set values.
Absolute standardized error of test samples plotted against their K j values.
The sum of squared error of test data is 1.953 and the percent incorrect predictions of test data is 8.3%%.
Figure 20c shows the normalized absolute average error of test data per normalized forecasting length (h). Figure 20d shows the divergence between test data and network test data.
Therefore, the SVR model is relatively insensitive to the limited number of train data, and the error of test data is also limited by the SVR model.
To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized.
As seen clearly from the plot, there had been a sharp decrease in the prediction error of test samples with an increase in K j.
The calculated p values (α = 0.05 except stated otherwise) of the post hoc tests were adjusted for the error of testing multiple, independent hypotheses (m) by applying a Bonferroni correction (i.e. setting the new p value to α/m).
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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