## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Advantages** & **Disadvantages** & **Computational Complexity** \\ \hline Principal Component & \begin{tabular}{c} Maximum dispersion \\ with low noise sensitivity, \\ parametric method \\ \end{tabular} & \begin{tabular}{c} Only good for linearly \\ separable data \\ \end{tabular} & O(N\({}^{3}\)) \\ \hline Linear Discriminant & \begin{tabular}{c} Maximum class \\ separation, \\ parametric method \\ \end{tabular} & \begin{tabular}{c} Suffers from class \\ singularity problems \\ \end{tabular} & O(N\({}^{3}\)) \\ \hline Kernel-PCA [50] & \begin{tabular}{c} It can be used for groups’ \\ nonlinear separation in \\ the feature space, \\ parametric method \\ \end{tabular} & There is no rule for \\ kernel and its parameters & \begin{tabular}{c} O(N\({}^{3}\)) \\ \end{tabular} \\ \hline t-distributed Stochastic & \begin{tabular}{c} t-SNE preserves \\ local structure \\ \end{tabular} & \
\begin{tabular}{c} Works well for only \\ 2–3 output variables, \\ nonparametric method \\ \end{tabular} & O(N\({}^{2}\)) \\ \hline Isomap [52] &
\begin{tabular}{c} This method maintains \\ pair-wise distances \\ between points \\ \end{tabular} & Nonparametric method & O(2N\({}^{3}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of DRMs. explainable data models for glioma diagnostics [31]. \"Black box\" methods, like deep neural networks, have a limited choice for interpretability analyses. The SHapley Additive exPlanations (SHAP) technique is one of the most renowned. SHAP is based on a solid theoretical base, but requires intensive computations as the cardinality of the data grows (except for tree-based models). The usage of RF, XGBoost, and linear kernel SVMs solves the problem of computational cost and a provides robust interpretability analysis. Various combinations of DRMs, ML, and feature selection methods form an ML pipeline [20]. The estimation of the efficiency of a created prediction data model is usually conducted using cross-validation methods. For two-class classification tasks, the results of the cross-validation step implementation are often presented in terms of the sensitivity, specificity, and accuracy metrics acceptable for a balanced dataset. They provide a visual comparison of various data models (mathematical formulas are provided in Supplementary Table S1). For a deeper data model efficiency analysis, a receiver operational characteristic (ROC) curve and corresponding area under the curve (AUC) method are preferable [59]. A ROC curve is a graph plotting the proportion of observations correctly predicted to be positive in all predicted to be positive ones versus the proportion of observations incorrectly predicted to be positive in all predicted to be negative ones. The definition of the ROC curve is as follows: a plot of the False Positive Rate (FPR, x-axis) versus the True Positive Rate (TPR, y-axis). The TPR is a probability of obtaining a positive result in the test when it is an actual positive. The FPR is the probability of obtaining a positive result in the test when it is actually negative. If a classifier gives the probabilistic score of prediction, a curve can be constructed. AUC is a quantitative measure of classifier performance. AUC values, which are close to 1 or 0, correspond to accuracy, where 0.5 is equivalent to guessing. In general, an AUC of 0.5 suggests no discrimination, 0.7 to 0.8 is acceptable, 0.8 to 0.9 is excellent, and more than 0.9 is outstanding. ROC-AUC provides easily interpretable visual metrics of binary classifier performance, however, it the drawback of underrating specificity. As many authors report, ROC-AUC is convenient for the comparison of several classifiers. For more precise estimates, sensitivity, specificity, and accuracy should be calculated along with ROC-AUC. A full description of the binary classifier performance contains the confusion matrix, but it has four different parameters, and it is hard to compare all of them simultaneously.