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We evaluated the credibility of the datasets in prediction models.
The study variables were selected from the datasets in the MEXT final assessment report.
The datasets in our case studies are fairly diverse in topicality, time span, and size, as shown in Table 1.
The way to introduce the datasets in WEKA is using the Attribute-Relation File Format (ARFF) files.
Figure 13 illustrates the approximation space (A^_{1-4}), considering all bnodes in the datasets in Figure 12.
Scalability on the domain dimension is a special concern for the datasets in which most of the features are categorical.
The datasets in Table 2 were normalised between 0 and 1 using WEKA [52] and contained no missing values.
The principal component analysis (PCA) identified three major components from the 9 major variables accounted for 80.05 and 86.68 of the datasets in HECW and VFCW, respectively.
Notably, results for the datasets in this works are higher than those for social networks in [28], and are actually comparable to reciprocity in neural networks.
The datasets in these collections typically come from different sources, are in disparate formats, and are stored in separate physical sites and in different types of repositories.
Further, we note that distribution of scaffolds in all the datasets in highly skewed with large number of them occurring only once (singletons).
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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