## 3 Results  
Based on the train-test split function, the dataset was randomly divided into training and test data in an 80% to 20% ratio. The training dataset was pre-processed using a standard scaler algorithm (i.e., the features were standardized by removing their mean and scaling them to unit variance). Then, the ADASYN algorithm was applied to balance the training dataset. The scaling function determined for the training dataset was used to scale the test dataset. In the manner described above, ten independent sets of training and test data were prepared. The size of randomly selected datasets is shown in Table 1. \(P\) stands for the number of rock bursts, and \(N\) stands for the number of non-destructive tremors. The value in brackets indicates the number of rock bursts after balancing the training dataset. Ten models of each of the mentioned machine learning algorithms (i.e., MLP, AdaBoost, GB, KNN, GNB classifiers, and ensemble classifier) were trained and tested on these datasets. The use of binary classification (i.e., positive for rock bursts and negative for cases without damage and destruction to the openings) enabled the use of basic model quality assessments such as true positive (\(T_{P}\)), true negative (\(T_{N}\)), false positive (\(F_{P}\)), and false negative (\(F_{N}\)). The \(T_{P}\) and \(T_{N}\) are the numbers of correctly classified positive and negative cases, respectively. The \(F_{P}\) is the number of samples incorrectly classified as positive but actually negative, representing the number of false alarms. In the case of \(F_{N}\), the situation is reversed and concerns the underestimation of positive cases. The model efficiency parameters were calculated based on the mentioned basic parameters such as accuracy, recall (sensitivity, true positive rate), precision (positive predictive value), and F1-score. The first of these parameters is the ratio of all correctly classified samples to all samples in the dataset:  
\[\text{Accuracy}=\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{N}+F_{P}} \tag{7}\]  
The recall is the ratio of \(T_{P}\) to all positive cases in the dataset. It can be calculated as follows:  
\[\text{Recall}=\frac{T_{P}}{T_{P}+F_{N}} \tag{8}\]  
Precision describes the degree to which positive predictions can be trusted. It is the ratio of \(T_{P}\) to all cases classified by the model as positive:  
\[\text{Precision}=\frac{T_{P}}{T_{P}+F_{P}} \tag{9}\]  
F1-score is the harmonic mean of precision and recall, which are assumed to be equally important. F1-score is calculated according to the following formula:  
\[\text{F1-score}=2\frac{\text{Recall}\text{-Precision}}{\text{Recall}+\text{ Precision}}=\frac{2T_{P}}{2T_{P}+F_{N}+F_{P}} \tag{10}\]  
The efficiency characteristics of the models of selected classifiers: MLP, AdaBoost, GB, KNN, GNB, and ensemble hard-voting classifier are presented in Tables 2-7, respectively. For a comparison of the trained and tested models of machine learning algorithms, the receiver operating characteristic (ROC) curve was also used (Figure 3). The ROC curve is created by plotting the recall (true positive rate) against the false positive rate, and it illustrates the ability of a binary classifier as its discrimination threshold is varied. The more accurate the model, the more the ROC curve is shifted toward the upper-left corner. The diagonal represents a random classifier. The area under the ROC curve is an indicator describing the effectiveness of the model. The values of the area under the ROC curves for the models of the selected machine learning algorithms are shown in Table 8.