## 4 Discussion  
The risk of rock bursts is still a significant problem in mines located in the USCB and around the world. The article presents the possibility of classifying rock bursts in a selected hard coal mine using examples from the past. The investigation of rock bursts in coal mines in the USCB shows that they mostly occur in longwall galleries (maingates or tailgates) and drilled headings. However, in recent years, rock bursts have also occurred in the opening-out headings, usually in the protective pillars, especially when the longwall mining was ending in their area. This article presents the results of the classification of rock bursts and non-destructive tremors in an active underground coal mine using machine learning algorithms. The methodology used allowed for the correct classification of rock bursts in the conditions of numerous non-destructive tremors and in the complex geological and mining conditions of an underground coal mine. Geological, geomechanical, mining, and technical parameters were taken into account. The first two types of parameters were included in one GEO index, adapted to the conditions prevailing in underground mines in the USCB and a rock burst database collected following the Polish mining regulations. The selected points were then correlated with tremors in their vicinity, and the PPV values at these points were calculated. Such a comprehensive use of the selected parameters was applied to assess whether a rock burst may occur at a given point in the opening. However, the presented method could be applied to assess the potential risk of rock bursts for openings in underground coal mines. Efforts were made to at least partially reflect the real ratio of rock bursts to most cases of tremors that did not cause damage to the openings. The database contained 627 samples. The share of rock bursts in the current database was only about 8.5%. As a result, the dataset was highly imbalanced, and cases of tremors without damage to the openings predominated. The parameters related to the occurrence of rock bursts did not show a linear relationship (i.e., an increase in a given parameter does not necessarily have to be associated with the occurrence of rock bursts in the opening). Many parameters influence the occurrence of a rock burst in a specific configuration, and a large number of them were included. Balancing the dataset with the ADASYN sampling algorithm was necessary for the correct classification of minority rock burst events. Since the occurrence of rock bursts and the range of damage are not linear, machine learning algorithms were used. Machine learning algorithms including MLP, AdaBoost, GB, KNN, and GNB classifiers are particularly suitable for solving nonlinear issues. The average accuracy for the models of applied algorithms equaled: 0.92 for MLP, 0.9 for GB, 0.87 for AdaBoost, 0.85 for KNN, and 0.77 for GNB. The average recall of the models equaled 0.67 for GNB, 0.63 for GB, 0.62 for MLP, 0.6 for KNN, and 0.56 for AdaBoost. The average precision of these models ranged from 0.24 (for GNB) to 0.54 (for MLP). The average F1-score of these models was between 0.34 (for GNB) and 0.57 (for MLP). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Dataset** & **MLP** & **AdaBoost** & **GB** & **KNN** & **GNB** & **Ensemble Classifier** \\ \hline
1 & 0.63 & 0.63 & 0.57 & 0.28 & 0.42 & 0.60 \\
2 & 0.77 & 0.52 & 0.52 & 0.66 & 0.58 & 0.77 \\
3 & 0.53 & 0.53 & 0.64 & 0.55 & 0.46 & 0.69 \\
4 & 0.57 & 0.33 & 0.53 & 0.43 & 0.37 & 0.48 \\
5 & 0.56 & 0.43 & 0.60 & 0.52 & 0.42 & 0.65 \\
6 & 0.56 & 0.52 & 0.63 & 0.46 & 0.55 & 0.68 \\
7 & 0.64 & 0.45 & 0.55 & 0.65 & 0.49 & 0.65 \\
8 & 0.68 & 0.60 & 0.69 & 0.46 & 0.51 & 0.74 \\
9 & 0.47 & 0.46 & 0.57 & 0.41 & 0.43 & 0.58 \\
10 & 0.55 & 0.41 & 0.40 & 0.41 & 0.45 & 0.53 \\ \hline mean & 0.60 & 0.49 & 0.57 & 0.48 & 0.47 & 0.64 \\ \hline \end{tabular}
\end{table} 
Table 9: Area under precision–recall curves for models of selected machine learning algorithms.