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Table 6 includes a summary for comparison of our results with that of the previous studies which have used the same datasets with our study.
Different studies have used the same datasets, learners, and performance metrics but achieved different results due to different feature engineering methods; [3] and [25] or [23] and [11] are examples.
Several experiments used the same datasets (built by Ott et al. [3] using AMT) and show that for this dataset, the highest performance is achieved using bigrams and LIWC [3, 11, 12].
Wherever possible we also used the same datasets.
We also used the same datasets in [ 8].
To test their performance, we used the same datasets as those used in the PAGAN article (Löytynoja et al., 2012).
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I even used the same dataset for several workshops.
For hindcasting during the 20th Century, we used the same dataset from 1901 onwards.
We used the same dataset used in S3, and our results corroborated most of those obtained in S2.
We used the same dataset from the PCA to calculate k-means clustering to understand the number of partitions and their means given our data.
Hypomania used the same dataset as28, in which the presence/absence of hypomanic episodes had been coded for each subject by trained clinical raters.
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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