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For the purposes of the probabilistic model, we modelled the sensitivity and specificity on the log odds scale using normal distributions.
The first study (Menden et al., 2013) modelled the sensitivity of 608 cell lines to 131 drugs with Neural Networks and Random Forests, thereby obtaining a coefficient of determination (R) of 0.64 on an external set.
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Therefore, our findings model the sensitivities seen in migraineurs.
For all scenarios modelled in the sensitivity analysis, nVNS + SoC was more effective and cost saving (Table 6).
Contextualists differ over how to model the context-sensitivity.
In order to verify the validity of the models, the sensitivity analysis is conducted and discussed.
With the well validated ATLAS model, the sensitivity analysis of the system heat loss on the SBO transient is performed.
With this implicit model, the sensitivity of steady states to parameters can be investigated efficiently using continuation methods.
With these variables in the predictive model the sensitivity of the derived rule for predicting failure was 77%, the specificity was 65%, and the overall accuracy was 69%.
From the visual model, the sensitivity can be easily worked out: (13).
As a measure for the appropriateness of the models, the sensitivity, specificity, balanced accuracy, and P-value are reported for all models when applied to the whole dataset.
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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