## 1 Introduction
A solution is to use parametric or nonparametric dimensionality reduction methods (DRMs). Parametric DRMs use an explicit association between the initial feature space and the new space of less dimensionality, but they do not work well when the data form non-hyperspherical clusters. Nonparametric DRMs effectively reduce the dimensionality of data of any nature, but they cannot be applied to new data, that is, they can only be used retrospectively. DRMs also provide informative feature selection. Table 1 describes the most popular DRMs. The computational complexity of these algorithms depends on N--the number of input data points. Here, O(N) means that the number of required computational operations (like addition or multiplication, etc.) is of the N order. According to the literature, the most useful DRMs are PCA and t-SNE [48; 51]. The PCA algorithm reduces the number of variables in the dataset, but keeps as much information as possible. It is achieved by finding a set of new orthogonal variables and ranking them by the amount of variance they explain. Then, the number of reduced variables is chosen, according to a user-defined value of the explained variance. Below, the analysis is limited by 10 principal components to have an ability for data spatial distribution visualization. The t-SNE explores similarities between data points in a feature space using the joint probabilities of two data points and selecting others as their neighbors, and then tries to find the best mapping of these points, preserving the original feature similarities in a low-dimensional space. Essentially, t-SNE constructs new metrics depending on the input data. Usually, the best results of PCA application are achieved with preliminary data standardization by subtracting the mean and dividing by the standard deviation. This preprocessing step is useful when data have a high variability that affects new axes calculations in PCA. The t-SNE algorithm considers the distances between points in the original space, so any transformations may cause incorrect interpretations. But t-SNE is sensitive to the choice of metrics. Usually, cosine metrics provide a better performance for a high-dimensional feature space. At the stage of data modeling, supervised or unsupervised ML methods can be used. The primary criterion for choosing an ML method in the current research was explainability--an explicit relationship between input features and an output data model [53]. Unsupervised ML algorithms based on using distance metrics between data points in hyperspace usually provide unsatisfactory results, while the results of applying hierarchical, density-based techniques are hard to explain [54]. A general rule for ML methods is that the higher complexity of the model, the more difficult interpretation is. From the point of view explainability, supervised ML (SML) algorithms can be roughly classified into \"white box\" (has intrinsic tool for interpretation) and \"black box\" (requires an external tool for interpretation) [55] ones. \"White box\" methods with embedded feature selection functions are preferable because of their good computation performance and the stability of their results [56]. They include methods such as linear kernel Support Vector Machines (SVM), decision trees (DT), Random Forest (RF), and variants of the Extreme Gradient Boosting Machine (XGBoost) constructed from DTs [57]. SVMs are based on finding a hyperplane, which optimally separates the data points of two classes [58]. Both RF- and DT-based XGBoost were proven to be reliable techniques for constructing