## 5 Classification by Groups of Features
### Symbol-Based Classification  
In this work, the features were formed on the basis of single characters, as well as their 2 g, 3 g, and 4 g, and their various combinations. As the endings of lines have a special meaning for poems, the line feed was also taken into account. The final results for this group are presented in Table 8. The best result for single characters was obtained using the ExtraTreesClassifier method, the AUC ROC value was 0.8177. Further, the features were formed on the basis of single characters and 2 g and only 2 g; the AUC ROC value was higher when classified on the basis of only 2 g. As the classification based on bigrams is more accurate, and the addition of individual symbols to bigrams did not lead to an increase in the quality of classification, it was decided to abandon the usage of single symbols as features in the future. It should be noted that, with the increase in the number of features, the classifiers based on neural networks and the support vector method began to show significantly better results. Next, the classifications based on 2 and 3 g and only 3 g were considered. In both variants, there is a significantly greater number of features. The comparison of these two classifiers shows that the best accuracy is demonstrated by a classifier based on 2 and 3 g using the support vector method, in this case the AUC ROC value is 0.8819, and the balanced accuracy exceeds 80%. It can be concluded that 2 g are the important features for classification; therefore, for the next attempt to improve the quality of classification, 2, 3, and 4 g were used together. As a result, the best classifier in this group was obtained, the AUC ROC value for it was already 0.8886, but at the same time the number of features increased to 64495. A further increase in the number of n-grams did not lead to an increase in accuracy. The classification based on a dataset with the poems of at least 16 lines shows even better results, they are presented in Table 9. Here, for the best case of classification, the AUC ROC increased by more than 2% compared to the previous case and amounted to 0.9161; the best indicators are also demonstrated by balanced accuracy and F1. The Figure 4 describing the importance of features for classification by 2, 3, and 4 g of characters using the Shepley method is shown in the figure, for clarity, the spaces on it are replaced by underlines, and the line feed is replaced by ENTER\({}_{\sim}\). The analysis of n-grams of characters shows that [PERSON]'s poems are not characterized by the usage of an exclamation mark both at the end of the line and in the middle (that is, if the exclamation \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Method** & **AUC ROC** &
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} & **F1** \\ \hline prepositions, conjunctions, interjections, and particles & LR & 0.7102 & 0.6733 & 0.6037 \\ \hline prepositions, conjunctions, interjections, particles, and pronouns & LR & 0.6985 & 0.6038 & 0.5636 \\ \hline all words, no more than three letters long & RF & 0.6701 & 0.6291 & 0.5721 \\ \hline all words, no more than five letters long & LR & 0.7434 & 0.6932 & 0.6212 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The classification results by service and short words for the main data set. mark is not followed by a line translation), but it is very characteristic to use a dot and an ellipsis at the end of a sentence and at the end of a line, as well as a dash at the end of the line. [PERSON]'s poetic style is also characterized by the usage of words ending in -111 and -111 and lines in -111. This statement is in good agreement with the result established by the classification according to words, stating that [PERSON] used adjectives more than other poets. In addition, the presented diagram confirms the statement that the widespread usage of the prepositions characterizes the work of the other poets in our sample to a greater extent, especially the prepositions \(\kappa\), \(\beta\), and \(\varsigma\).