## 4 Experiment and Result Analysis
### Results and Comparison  
In this section, we summarize the overall comparison results that were attained over various machine learning methods. Table 3 shows the results of the classifiers on imbalanced and balanced data among all subjects and highest performance of evaluation metrics are marked in bold. As shown in Table 3, the KNN classifier performed the worst type II error of 0.2490 and F-score of 0.7604. On the contrary, the MLP classifier obtained the best type II error of 0.0500 and SVM achieved the highest F-score of 0.8343. As can be seen, it is hard to build the prediction model with a high geometric mean in terms of the highest type I error in imbalanced data; moreover, this error indicates that successful smoking quitters are misclassified as failed during the intervention. Dealing with the type I error issue under the class imbalance problem, we have applied the SMOTE and ADASYN oversampling techniques. The comparison result shows that the SMOTE with GBT classifier achieved the highest F-score of 0.8888, a balanced accuracy of 0.7928, a geometric mean of 0.7868 and a low type I error of 0.3044 when the learning rate was 0.005 and the number of the tree was 750. The lowest type II errors of 0.0405 and 0.0440 were achieved by the SMOTE with deep MLP, MLP classifiers, respectively. With regard to the evaluation metrics, SMOTE with GBT, MLP classifiers reached the highest results in all subjects. In this study, our experimental dataset includes 3303 (89.46%) men and 389 (10.54%) women, whereas the former smokers' target includes 856 (23.20%) men and 95 (2.60%) women, and the current smokers' target includes 2447 (66.30%) men and 294 (8.00%) women. It is clearly shown that men have a tendency to use tobacco at higher rates than women; moreover, it is important to construct the prediction models for each gender. The evaluation results of prediction models among men and women are summarized in Tables 4 and 5. Figure 2: Results of the variance inflation factor analysis on the selected features. \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 & 0.4754 & 0.1971 & 0.6579 & 0.5725 & 0.5241 \\  & MLP & 0.4499 & 0.1496 & 0.6842 & 0.5831 & 0.5182 \\  & Deep MLP & 0.4522 & 0.1684 & 0.6814 & 0.5751 & 0.5147 \\  & RF & 0.7983 & 0.5000 & 0.2818 & 0.6091 & 0.5992 \\  & GBT & 0.8046 & 0.5055 & 0.2727 & 0.6109 & 0.5997 \\  & KNN & 0.2614 & 0.0474 & 0.8455 & 0.5536 & 0.3836 \\  & SVM & 0.2350 & 0.0365 & 0.8636 & 0.5500 & 0.3625 \\ \hline \multirow{8}{*}{SMOTE} & LR & 0.8312 & 0.3827 & 0.2042 & 0.7066 & 0.7009 \\  & MLP & 0.7461 & 0.3698 & 0.3132 & 0.6585 & 0.6579 \\ \cline{1-1}  & Deep MLP & 0.7305 & 0.3735 & 0.3340 & 0.6463 & 0.6459 \\ \cline{1-1}  & RF & 0.8952 & 0.2405 & 0.1364 & 0.8116 & 0.8099 \\ \cline{1-1}  & GBT & 0.7804 & 0.1970 & 0.3282 & 0.7374 & 0.7345 \\ \cline{1-1}  & KNN & 0.7849 & 0.2307 & 0.2909 & 0.7392 & 0.7386 \\ \cline{1-1}  & SVM & 0.7336 & 0.3523 & 0.3655 & 0.6411 & 0.6411 \\ \hline \hline \multirow{8}{*}{ADASYN} & LR & 0.8342 & 0.4237 & 0.2028 & 0.6868 & 0.6778 \\ \cline{1-1}  & MLP & 0.7607 & 0.3984 & 0.3605 & 0.6206 & 0.6203 \\ \cline{1-1}  & Deep MLP & 0.7527 & 0.4188 & 0.3619 & 0.6097 & 0.6090 \\ \cline{1-1}  & RF & 0.8926 & 0.2627 & 0.1373 & 0.8000 & 0.7975 \\ \cline{1-1}  & GBT & 0.7600 & 0.1558 & 0.3223 & 0.7610 & 0.7564 \\ \cline{1-1}  & KNN & 0.7709 & 0.1842 & 0.3086 & 0.7536 & 0.7510 \\ \cline{1-1}  & SVM & 0.7209 & 0.3263 & 0.3586 & 0.6576 & 0.6574 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of the prediction models among men.