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Exact(39)
We use Turkish and English review datasets in our experiments.
We have considered the subset of the entire datasets in our experiment.
The datasets in our case studies are fairly diverse in topicality, time span, and size, as shown in Table 1.
For both datasets in our evaluation, we estimate (MA = 10) ppm, (m_{ MA } = 200) Da, and (sigma _{text{m}}= 10) by manual inspection of the data.
That is why we tried different combinations of feature selection methods and text classifiers on multiple datasets in our research so that we can compare their performance collectively and accurately.
Beta1AR, beta3AR, and HIVi are very small datasets in our comparison; thus, it seems probable that the poor results of the Bayesian approach (a poor approximation of the (mathcal {S}) value) were caused by the high internal variance in the dataset rather than because the Bayesian approach was actually worse than the grid search method.
Similar(21)
So it is a single-label dataset in our learning problem.
We investigate each dataset in our statistical pipeline and tweak various parameters.
This forms the first chemical dataset in our study.
The reference population used for this standardisation was the complete 20 country dataset in our study.
We are exploring the utility of some of these modeling approaches in the current dataset in our parallel ongoing research.
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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