## Numerical Analysis
### Comparison between the Control and Experimental Groups  
Table 5 lists the results of comparing the control and experimental attribute groups over that of the training and test sets. During the modeling phase using the training set, k-NN performed best in CTRL (99.66%), EXP2 (99.73%), and EXP3 (99.32%). LR was best in EXP1 (99.82%); however, it was second best in other groups. During the validation phase using the test set, LR showed the best results in all groups, and k-NN was second best. Thus, in addition to LR, k-NN could be considered as a suitable classifier for the APS dataset. Considering the scores obtained for accuracy, F-measure, and MCC, the combination of EXP2 and LR outperformed the other attribute groups and classifiers. The stability of a prediction model must be considered for practical applications. Thus, k-NN can be considered as a potential candidate. If LR failed on the prediction or underperformed, then k-NN can be replaced. To get further insights into the models' performance, decile-wise lift charts were employed to demonstrate the level of the prediction capability that can be produced by examples fed into the trained model. If few examples can produce a high prediction capability, then the calculated lift value is regarded to be high. Figure 3 shows the decile-wise lift charts to demonstrate how LR and k-NN perform by sample size (represented in percentages). It can be noticed from Figure 3a that both LR and k-NN performed well when predicting the NEG class label. After inputting 80% of the examples of the test set, the performance of both the LR and k-NN models decreased. For the POS label, it can be noted from Figure 3b that the validation performance of LR was better than the k-NN in the first 10% of the test examples. While increasing test examples to 20% and up to 80%, the validation performance of k-NN was more stable because the calculated lift values were greater than 1.000. The valid test examples of EXP2 were 12,885 and 10% \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Classifier**} & \multicolumn{3}{c}{**Training Set (for Modeling)**} & \multicolumn{3}{c}{**Test Set (for Validation)**} \\ \cline{3-8}  & & **Accuracy** & **F-Measure** & **MCC** & **Accuracy** & **F-Measure** & **MCC** \\ \hline \multirow{5}{*}{CTRL} & LR & 99.58\% & 66.57\% & 67.45\% & 99.40\% & 66.67\% & 68.18\% \\  & SVM & 99.42\% & 37.80\% & 46.85\% & 99.15\% & 38.81\% & 47.93\% \\  & k-NN & 99.66\% & 72.04\% & 73.90\% & 99.26\% & 55.23\% & 58.41\% \\  & GBT & 98.91\% & 58.89\% & 60.53\% & 98.44\% & 55.36\% & 58.22\% \\  & FR & 98.33\% & 0.00\% & 0.00\% & 97.66\% & 0.00\% & 0.00\% \\ \hline \multirow{5}{*}{EXP1} & LR & 99.82\% & 76.88\% & 77.57\% & 99.69\% & 67.29\% & 68.23\% \\  & SVM & 99.66\% & 38.98\% & 49.12\% & 99.60\% & 44.44\% & 53.34\% \\  & k-NN & 99.76\% & 65.51\% & 69.15\% & 99.63\% & 53.93\% & 59.17\% \\  & GBT & 98.94\% & 61.68\% & 62.56\% & 98.58\% & 59.49\% & 61.37\% \\  & FR & 98.33\% & 0.00\% & 0.00\% & 97.66\% & 0.00\% & 0.00\% \\ \hline \multirow{5}{*}{EXP2} & LR & 99.68\% & 72.60\% & 73.44\% & 99.56\% & 73.24\% & 74.30\% \\  & SVM & 99.49\% & 41.43\% & 49.74\% & 99.28\% & 42.24\% & 51.00\% \\ \cline{1-1}  & k-NN & 99.73\% & 76.23\% & 77.47\% & 99.44\% & 63.64\% & 65.91\% \\ \cline{1-1}  & GBT & 98.93\% & 60.48\% & 61.65\% & 98.51\% & 58.86\% & 60.89\% \\ \cline{1-1}  & FR & 98.93\% & 0.00\% & 0.00\% & 97.66\% & 0.00\% & 0.00\% \\ \hline \multirow{5}{*}{EXP3} & LR & 99.29\% & 59.47\% & 60.61\% & 99.08\% & 62.09\% & 63.23\% \\  & SVM & 99.08\% & 25.86\% & 36.98\% & 98.79\% & 31.18\% & 42.71\% \\ \cline{1-1}  & k-NN & 99.32\% & 59.01\% & 61.25\% & 98.89\% & 48.45\% & 51.91\% \\ \cline{1-1}  & GBT & 98.86\% & 56.15\% & 58.04\% & 98.41\% & 54.64\% & 57.45\% \\ \cline{1-1}  & FR & 98.33\% & 0.00\% & 0.00\% & 97.66\% & 0.00\% & 0.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance results of the five considered classifiers for the control and experimental attribute groups over training and test sets.