## 7 Analysis and Discussion of Results of Computer Experiments
### Comparative Analysis of Different Types of Features
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author Number** & **Precision** & **Recall** & **F1-Score** & **Number of Instances** \\ \hline
1 & 1 & 1 & 1 & 3 \\
2 & 1 & 1 & 1 & 47 \\
4 & 1 & 1 & 1 & 3 \\
5 & 1 & 1 & 1 & 5 \\
6 & 1 & 0.933333 & 0.965517 & 15 \\
7 & 0.952381 & 1 & 0.97561 & 20 \\
8 & 1 & 1 & 1 & 3 \\
9 & 0.925926 & 1 & 0.961538 & 25 \\
10 & 1 & 0.875 & 0.933333 & 16 \\
11 & 1 & 1 & 1 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recognition results per different authors with n-gram frequencies on literary works. Figure 8 shows the maximum recognition accuracies obtained by various machine learning methods and models: from left to right, the first column shows text sets (the bases on which recognition accuracies are calculated), the second column shows machine learning methods and models, and the third column shows recognition accuracies. From this figure, it can be seen that higher recognition accuracies were obtained using SVM and RF than using ANN. From the recognition accuracies obtained with 75% confidence on the test set, it is clear that SVM recognizes with greater confidence than RF. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author Number** & **Precision** & **Recall** & **F1-Score** & **Number of Instances** \\ \hline
1 & 1 & 1 & 1 & 3 \\
2 & 0.959184 & 1 & 0.979167 & 47 \\
4 & 0.75 & 1 & 0.857143 & 3 \\
5 & 1 & 1 & 1 & 5 \\
6 & 1 & 1 & 1 & 15 \\
7 & 1 & 0.95 & 0.974359 & 20 \\
8 & 1 & 1 & 1 & 3 \\
9 & 1 & 1 & 1 & 25 \\
10 & 1 & 0.875 & 0.933333 & 16 \\
11 & 1 & 1 & 1 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Recognition results per different authors with word frequencies on literary works. Figure 8: Maximum recognition accuracies obtained by different methods of machine learning. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Type** & **Text** & **Number of** & **Precision (Weighted** & **Recall (Weighted** & **F1 (Weighted** & **Cohen’s Kappa** \\  & **Set** & **Instances** & **Average**) & **Average**) & **Cohen’s Kappa** \\ \hline word frequencies & Train & 325 & 1 & 1 & 1 & 1 \\ word frequencies & Test & 78 & 0.943098 & 0.935897 & 0.93355 & 0.928819 \\ word frequencies & Works & 151 & 0.982329 & 0.980132 & 0.980217 & 0.975997 \\ n-gram frequencies & Train & 325 & 0.994319527 & 0.993846154 & 0.99389705 & 0.993187583 \\ n-gram frequencies & Test & 78 & 0.933699634 & 0.91025641 & 0.913092463 & 0.900763359 \\ n-gram frequencies & Works & 151 & 0.981428922 & 0.98013245 & 0.979912242 & 0.976025404 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overall recognition results. In the study, the results of the comparative analysis of using different text feature types with different machine learning techniques were obtained on the example of a dataset consisting of texts in the Azerbaijan language. Nevertheless, the approaches used in the study and the methodology of the conducted experiments can be used in the recognition of text authors in other languages as well. The models we have built depend strongly not only on linguistic features but also on the region (geographical location) of residence of the text authors. This was pointed out by [PERSON], [PERSON] et al., whose research is related to the dependence of the characteristics of the authors' texts on the region of their residence [37]. At the same time, we believe that the geographical factor is stable over time for the authors of texts, and the movement of authors geographically does not significantly affect the stylistic \"fingerprint\" of authors.