## 4 Experiment and Result Analysis
### Results and Comparison
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sampling** & **Model** & **F-Score** & **Type I Error** & \begin{tabular}{c} **Type II** \\ **Error** \\ \end{tabular} & \
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Geometric** \\ **Mean** \\ \end{tabular} \\ \hline \multirow{8}{*}{Non-sampling} & LR & 0.8016 & 0.9779 & 0.1027 & 0.4597 & 0.1408 \\  & MLP & 0.8048 & 0.9673 & 0.0500 & 0.4914 & 0.1763 \\  & Deep MLP & 0.7913 & 0.9701 & 0.0681 & 0.4809 & 0.1669 \\  & RF & 0.8148 & 0.8652 & 0.1763 & 0.4793 & 0.3332 \\  & GBT & 0.8179 & 0.9867 & 0.1600 & 0.4267 & 0.1057 \\  & KNN & 0.7604 & 0.7636 & 0.2490 & 0.4937 & 0.4214 \\  & SVM & 0.8343 & 0.9833 & 0.1040 & 0.4564 & 0.1223 \\ \hline \multirow{8}{*}{SMOTE} & LR & 0.8171 & 0.6852 & 0.1500 & 0.5824 & 0.5173 \\  & MLP & 0.8660 & 0.5125 & 0.0440 & 0.7218 & 0.6827 \\ \cline{1-1}  & Deep MLP & 0.8635 & 0.5456 & 0.0405 & 0.7069 & 0.6603 \\ \cline{1-1}  & RF & 0.8571 & 0.3074 & 0.1500 & 0.7713 & 0.7673 \\ \cline{1-1}  & GBT & 0.8888 & 0.3044 & 0.1100 & 0.7928 & 0.7868 \\ \cline{1-1}  & KNN & 0.8056 & 0.3137 & 0.2264 & 0.7300 & 0.7286 \\ \cline{1-1}  & SVM & 0.8410 & 0.4344 & 0.1300 & 0.7178 & 0.7015 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results of the prediction models among all subjects. For the data sample of men, the best model was distinguished by SMOTE with an RF classifier in terms of the f-score, type II error, balanced accuracy and geometric mean, which reached 0.8952, 0.1364, 0.8116 and 0.8099 when the number of trees is 250 and the criterion is entropy parameters. The SVM classifier performed the better type I error of 0.0365, but it performed the lowest geometric mean of 0.3625 due to worst type II error of 0.8636. Following this, the second best performances were yielded by the ADASYN with the RF classifier when the parameters for the number of trees was 250 and the criterion is gini, as shown in Table 4.  
As seen in Table 5, the prediction models of women were hardly affected by the imbalanced distribution because the highest type I errors were reached by the baseline classifiers. In terms of type I errors, classifiers performed the lowest balanced accuracy and geometric mean metrics. However, ADASYN with MLP classifier exhibited better balanced accuracy of 0.7735, the geometric mean of 0.7603 and lowest type I error of 0.3684, significantly. In addition, the best f-score of 0.9134 was achieved by the ADASYN with LR. Comparing to the evaluation performances are shown in Tables 3-5, certainly type I error issue is the biggest challenge in imbalanced data when it occurs our minority class for successful smoking quitters are misclassified as failed after smoking cessation intervention. Correspondingly, type II error occurs when current smokers are misclassified as successful quitters. Our comparison results revealed significant improvements in prediction models among all subjects and each gender when utilizing the SMOTE and ADASYN oversampling techniques. It is shown that geometric mean suits are one of the proper metrics for ascertaining between critical type I and type II errors. Therefore, concerning the geometric mean, Figures 3-5 illustrate the boxplot of the prediction models among all subjects, men and women. As depicted in Figure 3, the GBT classifier presented the worst score in non-sampling data, but the SMOTE and ADASYN-based GBT classifier displayed significantly better results among all samples. For the data sample of men, RF and GBT ensemble models reached the best performance when it served the synthetic oversampling techniques, as illustrated in Figure 4. By way of contrast, the worst performances were performed by KNN and SVM single classifiers among men. Furthermore, ADASYN