## 3 Results
### Quality Assessment of the Random Forest Machine Learning Algorithm  
The following presents the results of the quality assessment of the random forest classification analysis algorithm. Figure 2 shows graphs of the misclassification coefficients (\(Kmr\)) for successive steps of adding trees. Initially, the number of trees, \(tmax\) = 100, is given. The graphs show lines for training data and test data. As can be seen, it took at least 85 trees to achieve the lowest misclassification rate (\(Kmr\approx\) 0.5). This result is close to the prediction model with the best predictive validity. However, in the test samples for all variants of the investigated sets of decision trees in <<random forest>> (\(tmax\) = 50, 100, 150, 200, 250, 300, and 400) there is almost a 50% risk of misclassification of trees (interval \(A_{r}\) = (0.471651; 0.579786). Table 2 presents the risk estimation for the training and test samples. For our classification problem, where the condition is the presence of a categorical dependent variable (Pc_dep--level of digitization of telecommunication networks) and equal misclassification costs, the risk as the fraction of cases misclassified by trees is calculated. Figure 2: Plots of misclassification coefficients by successive tree addition steps (given number of solver trees, \(tmax\) = 100, \(tmax\) = 200, \(tmax\) = 300, and \(tmax\) = 400). The high value of the risk score and the significant difference in the error probability of the trained algorithm on the test sample objects compared to the training sample is probably indicative of the low generalizability of the learning algorithm due to overtraining. Note that machine learning researchers have indicated that samples with high noise or a given data set make the random forest model prone to overtraining [22,44]. In our case, the reason for overtraining is the high complexity of the model due to the unknown stochastic relationship between the objects (predictors) and the response (dependent categorical variables). Retraining the algorithm means taking a large amount of information from the raw data and using it in the model. In our problem, five predictors are used in the model: categorical Pc1 and continuous Pu2-Pu5. The three numerical continuous predictors (Pu3, Pu4, and Pu5) are highly correlated with the dependent categorical variable Pc_dep and show an importance (informativeness) of over 0.9 (Pu5 = 1.0; Pu4 = 0.99; and Pu3 = 0.95). The informativeness of the predictor Pu2 is moderate at 0.57. Most of the bagging trees use the strong predictors Pu3-Pu5 at their bases. Consequently, most <<random forest>> trees are similar, and the classification results are highly correlated. The resulting random forest model can be characterized as <<fine-grained>>, where a large number of variables can lead to complex processing. At the same time, there are no complex models and overfitting algorithms in classification problems, as in this case, which can be erroneous and tolerated. Additionally, taking into account the large number of input data on the situation in the regions is very important when solving management tasks of territorial development and the allocation of financial resources. The optimal value of trees _tmax = 300_, due to the lowest estimate of the risk of misclassification. Thus, we believe that the resulting model is adequate for the task of classifying regions according to the probability of developing intelligent transportation systems. Classes of Regions According to the Level of Capacity for Building Intelligent Transportation Systems Using the Random Forest Method  
As a result of performing all the sequential procedures of constructing a random forest with the number of decision trees t = 300, a given sample of regions is classified into four groups--classes according to the level of capacity to create intelligent transportation systems (ITS) (Table 3).