## 3 Results
### Predictive Classification Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameter** & **Range** & **Optimal Parameter** \\ \hline \multirow{3}{*}{\begin{tabular}{c} KNN \\ Algorithm \\ \end{tabular} } & n\_neighbors & *Range (1, 15, 1) & 4 \\  & Wights & ‘uniform’, ‘distance’ & ‘distance’ \\  & Algorithm & ‘auto’, ‘ball\_tree’, ‘kd\_tree’, ‘brute’ & ‘auto’ \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} SVM \\ \end{tabular} } & C & [0.05, 0.5, 1.0] & 0.5 \\  & Kernel & ‘linear’, ‘tbf’ & ‘linear’ \\  & n\_estimators & Range (10, 100, 5) & 10 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} RF \\ \end{tabular} } & max\_depth & Range (1, 15, 1) & 10 \\  & min\_samples\_leaf & Range (1, 10, 1) & 2 \\  & min\_samples\_split & [2,5,8,10,12] & 2 \\  & Criterion & ‘gini’, ‘entropy’ & ‘entropy’ \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of hyperparameter optimization using grid search and five-fold cross-validation. Accuracy, f1-score, sensitivity, specificity, PPV, and NPV were used to select the optimal predictive model among the six possibilities. As shown in Table 9, the average score for each model is approximately 30 analysis results, and the value in parentheses indicates the standard deviation. SVM showed the best performance in five out of six metrics (f1-score, accuracy, specificity, ppv, and npv). In addition to the above evaluation metrics, it was also confirmed through other methods. First, a receiver operating characteristic curve (ROC curve) shows performance per threshold using true positive rate (TPR) and false positive rate (FPR) values. Second, we used the area under the ROC curve (AUC), which means the value of the area under the ROC curve, and the value of AUC has a value between 0 and 1, and the closer it is to 1, the better the model. Finally, we used the Matthews correlation coefficient (MCC), which is a correlation coefficient used in binary classification, which has a value between \(-1\) and \(1\), and the closer it is to 1, the better the model. Figure 6 is a ROC curve generated based on the parameter with the best f1-score for each model, and AUC and MCC values are also expressed in the legend. As a result, it was found that SVM showed the best performance in the three metrics above. In the selected predictive model (SVM), important variables among the 15 odor substances were identified through variable importance (VI) [36, 37], as shown in Figure 7. Here, a variable with a large VI value is an important variable in generating a predictive model, and not a variable that greatly affects the fluctuation of a complex odor. Respectively, NH\({}_{3}\), PPA, and H\({}_{2}\)S showed the highest VI values, while DMDS and IVA showed the lowest. Summarizing the results of three analyses, the results are only shown for the four variables of NH\({}_{3}\), VLA, PPA, and BTA, which correspond to variables with high VI values, significant in the ANOVAs, and high correlation coefficients with complex odor among the 15 odor substances. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameter** & **Range** & **Optimal Parameter** \\ \hline Extra-Trees & n\_estimators & Range (1, 3, 1) & 3 \\  & max\_depth & Range (1, 7, 1) & 5 \\  & num\_leaves & [31,127] & 31 \\ \hline LightGBM & max\_depth & Range (3, 5, 1) & 3 \\  & min\_child\_weight & [\(1\times 10^{-7}\), \(1\times 10^{-3}\), \(1\times 10^{-1}\), \\  & min\_data\_in\_leaf & [30,50,100] & 30 \\ \hline XGBoost & max\_depth & Range (3, 10, 1) & 3 \\  & min\_child\_weight & Range (3, 5, 1) & 3 \\ \hline \hline \end{tabular}  
*Range (start, end + 1, step).  
\end{table} 
Table 8: _Cont._