## 4 Discussion
The use of an ensemble hard-voting classifier consisting of models of the five previously mentioned classifiers was proposed. The average accuracy value of the ensemble classifier models was 0.91, comparable to the highest average accuracy of the single classifier (i.e., 0.92 for MLP). Moreover, the average recall value of the ensemble classifier models was the highest (i.e., 0.74). The average precision of the ensemble classifier models equaled 0.51, and the average F1-score of these models equaled 0.6 and was the highest. The number of false alarms accounted for about 3.5% to 10.3% of all cases without damage to the openings (mean 6.9%). The ensemble classifier models had the highest average area under the ROC curve (i.e., 0.83). Additionally, the highest value of this parameter (i.e., 0.92) was in one of the models of this classifier. The area under the ROC curve for the remaining models ranged from 0.72 to 0.78 on average. The mean value of the area under the precision-recall curve was also the highest for the ensemble classifier models (0.64). However, only half of the ensemble classifier models had the highest area under the precision-recall curve. For the other models, it was smaller (3 cases) or equal (2 cases) in comparison with the single algorithm models. This article proposed a new approach to assessing the potential state of rock burst hazards in openings. It would be necessary to determine the geological, geomechanical, mining, and technical conditions at the points of the selected opening. By making certain assumptions, the trained models could be used to assess the potential risk of rock bursts in planned mine openings. The maximum predicted energy determined in accordance with mining regulations (e.g., by analytical, empirical or statistical methods) could be assumed as the tremor energy. Some assumptions about the location of the tremors' foci would also be necessary as this affects the calculated PPV. Usually, strong tremors are associated with the fracturing of a thick layer of sandstone deposited above the seam. Moreover, they usually occur close to remnants and edges in other seams, where rock layers are deformed. An important issue to consider in machine learning is the availability (quantity) and quality of data. The amount of data affects the generalization efficiency of the model. If there is too little data, especially for complex models, overfitting may occur. The study conducted for the dataset used did not show evidence of this phenomenon. Another limitation is the representativeness of the data. The database used contains records from one coal mine, so the resulting models were adapted to local conditions. Applying them without additional training to other mines may prove problematic. However, some general patterns in the trained models cannot be ruled out either. Moreover, it cannot be ruled out that the effectiveness of the models will deteriorate as the number of non-destructive tremors in the database continues to increase (i.e., the actual ratio of non-destructive tremors to rock bursts approaches). For some rock bursts and non-destructive tremors, the parameter configurations used were very similar. Considering such cases could be the reason for differences in the effectiveness of the trained models. It cannot be ruled out that using a different parameter configuration would result in a better distinction between rock bursts and non-destructive tremors. It should be noted that the GEO index was created on the basis of a database of rock bursts in accordance with mining regulations (i.e., on the basis of dynamic phenomena causing the loss of functionality of mine openings). In the database used for the study, phenomena causing local destruction and damage of the openings were also considered to be rock bursts. An additional problem is the directionality of energy radiation from the source of the tremor, which depends on the focal mechanism. This can have a significant impact on the PPV value in the opening.