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Instead of a cross-validation scheme, these new samples were predicted using a class prediction scheme.
Test samples were predicted with a relative standard error of prediction of 0.38%, 7.65% and 0.93% for tap density (single component), shear and rheometer respectively.
The 18 external validation samples were predicted on the previously described calibration models, and summary statistics for the prediction of these samples are provided in Table 6.
Statistically significant distinctions in ecological condition among validation samples were predicted by the resultant spatial model.
Also, antioxidant activities of Mentha samples were predicted using QCAR model.
The dynamic responses including the impact load displacement curves and impact deformation of the samples were predicted based on finite element analyses at the microstructure level.
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In addition, a high number of false negative predictions were made, 57% of the CFS samples being predicted as healthy controls.
We analytically show the improvements in terms of quadratic distortion when the samples are predicted rather than simply replicated.
After that, the dot set samples are predicted by original dot original samples and watermarked cross samples, and the watermark bits are inserted into the dot set in Step 1.2.
The variability of the 8p23.1 orientation in 150 HapMap samples was predicted using this set of SNPs and was verified by FISH in a subset of samples.
For instance, for a dataset with the positive/negative sample ratio of 1/9, although all samples are predicted to be negative, the overall accuracy is still 90%.
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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