## 3 Results  
This study employed eight ML models including, four DT models, two artificial neural network models, and two Bayesian network models. These models included CHAID, CART, C5, QUEST, ANNMLP, ANNRF, BAYEIANTAN, and BAYESIAN MARKOV. It is worth mentioning that data were split into train (80%) and test (20%) partitions before the models' employment [22; 78]. There is no agreement on the partitioning ratio among different studies. Since the sample size used in this study was relatively small, this present study allocated 80% of the data for training because it is necessary to ensure that the models examine more samples and, therefore, probably finds a more desirable solution. Besides, if the training dataset is small, the models of interest cannot learn general principles, \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Unite**} & **Minimum** & **Maximum** & **Average Value** & **Standard Deviation** \\  & & **Value** & **Value** & & **Value** \\ \hline Vp & (m/s) & 321 & 4215 & 2225.44 & 1041.71 \\ Vs & (m/s) & 356 & 1893 & 1129.64 & 430.28 \\ MC & (\%) & 20.02 & 59.9 & 34.85 & 13.19 \\ LL & (\%) & 23 & 70 & 40.97 & 8.94 \\ PL & (\%) & 4 & 42 & 14.71 & 7.41 \\ Gs & (g/cm\({}^{3}\)) & 1.82 & 2.81 & 2.63 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The used input parameters for determining soil classifications. and consequently, the models will have unsatisfactory test set performance. The sample size of train data was 113 and test data was 38. This study used several criteria to assess the performance of models employed, including recall, precision, overall accuracy, gains chart, and agreement between the models employed. Table 3 shows the overall accuracy of the models employed. For the training dataset, the BAYESIANMARKOV model achieved the highest overall accuracy (100%) while the QUEST, ANNMLP, and ANNRF achieved the greatest accuracy (94.74%) for the testing phase. On the other hand, the BAYESIANMARKOV model obtained the lowest overall accuracy (34.21%) though the CHAID, CART, ANNMLP, ANNRF, and BAYESIANTAN obtained the lowest overall accuracy. The training recall and precision (Table 4) of the classifiers for each soil characteristic are shown in Figure 11. Notably, all classifiers predicted soil characteristics of \"3\" and \"4\" more accurately than other traits. Category \"1\" was generally less correctly predicted than the other soil attributes, though the BAYESIANMARKOV model predicted this category with high accuracy. Moreover, CART, ANNMLP, ANNRF, and BAYESIANTAN followed the same patterns of recall and precision. That is, attributes of \"3\" and \"4\" were predicted most accurately, followed by characteristic \"2\". It is also worth noting that in five models, including CART, ANNMLP, ANNRF, BAYESIANTAN, and BAYESIANMARKOV, the recall and precision were identical. Thus, the corresponding trend lines of recall and precision in each related diagram have coincided. As pointed out previously, a series of gain charts were used to assess the performance of the models employed. The gains are estimated through Gains\(=(\text{n/N})\times 100\). The n means the number of hits in quantile, and N represents the total number of hits. Here, it is requisite to notice that \"hit\" points to the success of a model to forecast the first value in the set. In gains chart, the perfect model with perfect confidence (where hits = 100% of cases) is denoted by the blue line. The oblique red line represents the at-chance model. Moreover, other models in the chart are implied by other lines in the midst. The area between a model and the red line can compare a model employed and the at-chance model. This area recognizes how much better an employed model is compared to the at-chance model. Besides, the space between a model proposed and the best model knows where a proposed model can be grown. In the gains chart, it is important to maximize the space between the models' curves and the at-chance model.