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We evaluated the selection and prediction model using the validation accuracies in a LOOCV procedure.
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When using the 'overall accuracy' validation metric, we found an optimal value for the prior information level, indicating that the standard deviation of the legacy data regression parameters should be multiplied by 10.
Using the accuracy profile approach, validation results gave a LOD and LOQ for dicentrine of 3 μg/ml and 10 μg/ml, respectively.
For the Cohn-Kanade database, the average recognition rate of the proposed approach is 92.20%, when using "Leave-One-Out" method, and 90.37% when using the cross-validation for estimating its accuracy.
Using external validation, the accuracy increased by the same proportions, while showing an overall accuracy of R2 = 0.59 for silt and R2 = 0.56 for clay.
Only two tumours were incorrectly classified in the cross-validation using the VFI classification algorithm (accuracy rate: 96%).
Finally, we used the independent validation set to determine the accuracy of the predictor.
Similarly, the cross-validation accuracy, using the same kernel, while testing both models using similar interfaces at C-level was 90.5% for ModelND and 77.0% for ModelNDNN.
The x-axis denoted the number of SNPs, and the y-axis denoted the 10-fold cross validation accuracies using these SNPs.
Then the finite difference method was used to show the validation and accuracy of predicted mud pressure and investigate the wellbore stability in different states of vertical, horizontal and deviated.
In independent validation, prediction accuracies using the 42 chromosome 5A SNPs from QTL region are higher than those from the 808 common SNPs for all models except BayesB and to some extent for SPLS.
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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