## 2 Methodology
\(P_{a}\)--population in the region;  
\(X3_{a}\)--proportion of digitalization of the regional telephone network:  
\[X3_{a}=\frac{Cdig_{a}}{Call_{a}},\ a=[1,r] \tag{3}\]  
where  
\(Cdig_{a}\)--is the number of digital nodes in the telephone network in the region,  
\(Call_{a}\)--is the total number of nodes in the telecommunication network in the region;  
\(X4_{a}\)--is the share of investment in the reconstruction and modernization of infrastructure in the total investment in fixed capital:  
\[X4_{a}=\frac{Iinf_{a}}{Iall_{a}},\ a=[1,r] \tag{4}\]  
where  
\(Iinf_{a}\)--is the amount of investment for the reconstruction and modernization of infrastructure in the region,  
\(Iinf_{a}\)--is the total amount of investment in the region,  
\(X5_{a}\)--is the proportion of public roads that meet regulatory requirements:  
\[X5_{a}=\frac{Rnorm_{a}}{Rall_{a}},\ \ a=[1,r], \tag{5}\]  
where  
\(Rnorm_{a}\)--is the length of the public roads that meet regulatory requirements,  
\(Rall_{a}\)--is the total length of public roads in the region,  
\(X6_{a}\)--is the proportion of nondepreciated fixed assets in transport, communications, and information:  
\[X6_{a}=\frac{Fan_{a}}{Fall_{a}},\ a=[1,r] \tag{6}\]  
where  
\(Fan_{a}\)--is the value of nondepreciated fixed assets in transport, communications, and information in the region  
\(Fall_{a}\)--total value of fixed assets in transport, communications, and information in the region. Step 2: Verification of data through statistical processing and selection of variables for random forest classification analysis: categorical dependent variable (Pc_dep), categorical (Pc), and continuous (Pu) predictors. Data processing can be implemented on the basis of the calculation and analysis of descriptive statistic indicators: sampling variance (Sv), standard error (Es), standard deviation (Ds), mean (Av), kurtosis (Ex), asymmetry (As), interval (Int), minimum (Min), maximum (Max), and number of objects (Ra). The calculation of the indicators according to generally accepted methods of statistical data analysis is carried out. Figure 1: The author’s methodology algorithm for solving the problem of classifying objects according to their potential for creating intelligent transportation systems using random forest machine learning. As a key indicator affecting the choice of variables, we will take the sample variance (Sv), calculated as the deviation of the sample data from the mean:  
\[S_{y}=\sum\frac{(x_{i}-x_{av})*2}{(n-1)} \tag{7}\]  
where  
\(X_{av}\) is the sample average of the indicator;  
\(X_{i}\) is the i-th element of the sampling frame for the indicator;  
\(n\) is the size of the sampling frame for the indicator. The variance indicates how much the data of a sample population deviate from its mean. Accordingly, the greater the variance, the greater the dispersion of the data. Let us set the following condition for moving the baseline indicator (XI-X6) into the category of a variable for classification analysis: if the sample variance (\(Sv\)) deviates from the mean (\(Av\)) by more than 10 times, the baseline indicator \(X_{i}\) cannot participate in the classification analysis of the <<random forest>>:  
\[if\ ^{\otimes_{n}/A_{\mathrm{Pr}}}>10,\ then\ x\
otin Pc_{dep},\mathrm{Pc}, \mathrm{Pu}. \tag{8}\]  
In contrast, in this condition, the indicator \(X_{i}\) can participate in <<random forest>> classification analysis as a variable. Step 3. The next step is the key step, where the random forest algorithm by the ensemble bootstrap method is implemented. This step consists of five sequential procedures. 3.1. Define the basic parameters for classifying objects:  
the number of trees (\(t\)),  
is the number of set parameters to select splitting (n_ss),  
maximum tree depth (max_td),  
splitting criterion (Cr).