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In simple terms, validation can be seen as a process to determine whether we are 'performing the correct test'.
The final outcome of the SVM training and validation can be seen in Figure 6B, where the areas under the ROC curve are plotted for each iteration.
In this case, validation can be seen as an approach to correct for all optimization procedures taking place while deriving the signature from the training data [ 8, 9].
An overview of the procedure used for primer validation can be seen in Figure 2. Universal mouse total RNA was reverse transcribed using random hexamers and the cDNA was used as a template.
However, validation can be seen from the Welsh Health Survey, in which there was no difference in the proportion of adults reporting 'not good' general health between respondents and refusers.
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The dimensions used in the validation process can be seen in Table 4.
A summary of all results for the discovery and validation data can be seen in Figure 3.
Our findings reveal significant relationships between both subjective and objective validation criteria as can be seen from Table 1. Figure 6 shows the relationships graphically.
The comparison between observed and calculated sediment yields generated by the SOM #5 for the calibration and validation data sets can be seen in Fig. 6 and 7, respectively.
For example, the lowest dose scheme results in 22 wrong decisions whereas the highest dose scheme results in only 2 wrong decisions when the 0.1 M. Sln1 dataset is used as validation dataset as can be seen in the upper left hand side corner of Fig. 13.
Validation of prior learning can be seen as a dynamic, developing method that is designated differently in different countries.
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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