## 3 Results  
The results of the experimental evaluation are presented in Table 3. The table shows the combinations of features that guaranteed the best result of the classification algorithms when the dataset consists of \(m\) features. For each number of features \(m\), the highest classification accuracy was found and all combinations of features that allowed this accuracy are presented in Table 3. Therefore, the number of combinations presented in the table for each value of m in the table may differ. For example, there are six best combinations with 6 features, and only one best combination with 8 features. The 'X' represents the features that are included in the combination. For example, for the size of the feature set \(m=2\), the best result was shown by the sets [_F14_, _F15_] and [_F13_, _F15_] with a classification accuracy of 0.9211 for the decision tree and the random forest classifiers. As we can see from the results presented in Table 3, the best classification accuracy of 0.9693 was provided by the sets of features where \(m=13\) and \(m=15\). In most cases, the best results were demonstrated by the random forest classifier, followed by decision tree and XGBoost. Although the best results were achieved using a large number of features, combinations with a smaller number of features showed only a few percent lower classification accuracy. As presented in Figure 2, starting from \(m=7\), the accuracy reaches 0.9605, and further increasing the number of features adds only 0.0088. The number of 'X' in the feature columns of Table 3 essentially shows how important this feature is in various combinations of features and leads to high classification accuracy. The significance of the features is presented in Figure 3, where the features are arranged according to the number of best combinations from Table 3 in which they have been used. Figure 2: Best classification accuracy for different combinations of features, depending on the size of the combination. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{13}{c}{**Number of Features**} & \multicolumn{13}{c}{**Features \({}^{\star}\) Used in Feature Combinations**} & \multicolumn{13}{c}{**Accuracy of Classifiers \({}^{\star}\)**} \\ \cline{2-13} \multicolumn{13}{c}{**Features**} & \multicolumn{1}{c}{**F1**} & \multicolumn{1}{c}{**F2**} & \multicolumn{1}{c}{**F3**} & \multicolumn{1}{c}{**F4**} & \multicolumn{1}{c}{**F5**} & \multicolumn{1}{c}{**F6**} & \multicolumn{1}{c}{**F7**} & \multicolumn{1}{c}{**F8**} & \multicolumn{1}{c}{**F9**} & \multicolumn{1}{c}{**F10**} & \multicolumn{1}{c}{**F11**} & \multicolumn{1}{c}{**F12**} & \multicolumn{1}{c}{**F13**} & \multicolumn{1}{c}{**F14**} & \multicolumn{1}{c}{**F15**} & \multicolumn{1}{c}{**F16**} & \multicolumn{1}{c}{**F17**} & \multicolumn{1}{c}{**F18**} & \multicolumn{1}{c}{**DT**} & \multicolumn{1}{c}{**RF**} & \multicolumn{1}{c}{**SGD**} & \multicolumn{1}{c}{**LR**} & \multicolumn{1}{c}{**CRB**} & \multicolumn{1}{c}{**MP**} & \multicolumn{1}{c}{**XCB**} \\ \hline \multirow{9}{*}{9} & - & X & X & X & - & - & X & - & X & - & X & - & X & - & X & - & 0.298 & **0.9649** & 0.8947 & 0.8772 & 0.9211 & 0.8947 & 0.9  
The best classification accuracies for the different number of features were achieved by 45 combinations. For example, all these combinations contain _F15--Indication of young domain_ (the top feature in Figure 3), which is the most significant feature. The second most significant feature is _F9--Presence of money back payment_, which is included in 39 combinations of features. The third is _F2--Top domain length_, the fourth is _F16--Presence of TrustPilot reviews_, and so on. The five most significant features are presented in all the best feature combinations with a size greater than 6. The results presented in this section allow us to conclude that even a small number of the most significant features is sufficient to achieve very high classification accuracy, avoiding complex computations and data analysis.