## 3 Models' Application
### SVM Models with Different Kernels
It is critical to note that the \"gain\" refers to the successfulness of a predictive technique to gauge the amounts higher than the middle point of the field's range (PPV > 0.557). Mathematically, the gain is calculated as follow:  
\[Gain\%=\frac{q}{w}\times 100 \tag{4}\]  
where, \"\(q\)\" refers to the quantity of hits in quantile and \"\(w\)\" shows the whole quantity of hits. In the diagram resulted from the gain calculation, the faultless model with tremendous confidence is denoted by the blue line, the diagonal red line denotes the accidental model, and the other lines in the middle denote the models utilized in this research. Generally speaking, the higher-level lines indicate higher prediction accuracy models, especially on the chart's left side. The domain within a red line model illustrates the gain difference between an applied and an accidental model. The domain mentioned above illustrates the superiority of an implemented versus accidental model. The range between an applied and the best model indicates areas of improvement for the applied model. The results of the gain's computation are presented in Figure 8. The results showed that BC-SVMSIG (the green line) had the worst gain for both the training and testing phases. The prominence of the input variables of different SVM models was identified and shown in Figure 6:0 As can be seen, all models except BC-SVMSIG identified distance as the most important predictor for the PPV prediction. Besides, BC-SVMRBF and BC-SVMLIN models similarly acknowledged \"distance\" as the most influential factor on the PPV. The \"stemming\" was recognized as an influential predictor only by BC-SVMPOL and BC  
Figure 6:1 Gain chart of the proposed models. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**TRAIN**} & \multicolumn{4}{c}{**TEST**} & \multirow{2}{*}{\begin{tabular}{c} **Train** \\ **Ranking** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Test** \\ **Ranking** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\(A\)-R** \\ \end{tabular} } \\ \cline{2-2} \cline{6-11}  & & & & & \(MAE\) & & \(R\) & & \(MAE\) & & \
\begin{tabular}{c} **Train** \\ **Ranking** \\ \end{tabular} & &
\begin{tabular}{c} **Test** \\ **Ranking** \\ \end{tabular} \\ \cline{2-11}  & **V** & \(R\) & **V** & \(R\) & **V** & \(R\) & & \(R\) & & \\ \hline BC-SVMRBF & 0.955 & 4 & 0.372 & 4 & 0.965 & 4 & 0.472 & 3 & 8 & 7 & 15 \\ BC-SVMPOL & 0.946 & 3 & 0.388 & 2 & 0.935 & 2 & 0.488 & 2 & 5 & 4 & 9 \\ BC-SVMSIG & \(-0.208\) & 1 & 10.793 & 1 & \(-0.342\) & 1 & 14.455 & 1 & 2 & 2 & 4 \\ BC-SVMLIN & 0.944 & 2 & 0.387 & 3 & 0.959 & 3 & 0.471 & 4 & 5 & 7 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance results and ranking values of different ML approaches applied in this study. SVMSIG models. While the former model identified the \"stemming\" as another significant variable, the latter identified the \"stemming\" as the most significant PPV predictor. The \"maximum charge per delay\" and \"powder factor\" were selected as an influential factor only by the BC-SVMSIG model.